Project 4 (Viva):

Longitudinal Effects of Multiple Pollutants on Child Growth, Blood Pressure and Cognition

Principal Investigator: Diane R. Gold Co- Principal Investigator: Matthew Gillman

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ABSTRACT - Project 4 a. EPA-RC2009-STAR-C1 b. Title: Longitudinal Effects of Multiple Pollutants on Child Growth, Blood Pressure and

Cognition c. Investigators: PI:Diane R. Gold, Co-PIs: Emily Oken and Joel Schwartz d. Institution: Harvard School of Public Health, Boston, MA e. Project Period and Location: 2010-2015, Boston, MA f. Project Cost: $1 Million g. Project Summary: 1) Objectives/Hypotheses: Elevated blood pressure, reduced cognition, behavioral problems, and abnormal somatic growth are significant burdens on individuals, their families and society. We hypothesize that prenatal and postnatal pollution exposures (individual pollutants, sources, or mixtures) will lead to adverse changes in somatic growth, increased blood pressure, reduced cardiovascular fitness, and reduced cognition in children. The strength of the chronic and acute effects of individual pollutants on our outcomes will vary by source and mixture, as well as the timing of prenatal and postnatal exposures. Increased vulnerability or susceptibility to pollution effects on these adverse health outcomes will also result from socioeconomic disparities, stress and violence, environmental tobacco smoke, and reduced maternal and child omega-3 fatty acid intake measured in the prenatal as well as postnatal periods. 2) Experimental approach: We will test these hypotheses using Project Viva, a unique ongoing pre-birth cohort of over 1,300 children from Greater Boston with longitudinal repeated measures of somatic growth, blood pressure, and cognition. Families were recruited between 1999 and 2002, during the first trimester of pregnancy. Primary longitudinal growth outcomes for Project Viva will include weight-for-length z-score and change in weight-for-length (birth through to age 2); body-mass index z-score and change in body-mass index (2 yr through 10 yr of age). Blood pressure is measured at birth, 6 months, 3 yr, and 7 yr; cardiovascular fitness is assessed by Step Testing at 7 yr. Cognition is assessed as visual memory at 6 mo, 3 yr and 7 yr; language at 3 yr and 7 yr; intelligence at 7 yr; and behavior at 7 yr. Chronic systemic inflammation is a well-documented risk factor for high blood pressure and atherosclerosis, and dysregulation of growth. Our Secondary Aim is to explore the effects exposures to individual pollutants, sources, and mixtures on intermediate immune and endocrine responses, including cord blood mononuclear cell (CBMC) lymphoproliferative responses, and innate (IL-6, TNF-α), adaptive Th1(IFN-γ), and Th2(IL-13) CBMC responses to stimulation with the mitogen PHA; dust mite, and cockroach allergen; allergic sensitization (3 and 7 yr) and inflammation-related adipokines (leptin and adiponectin) levels (birth, 3 and 7 yr). 3) Expected Results: Cognitive deficits and child behavior problems not only impose costs and burdens on children and their families, but also on their school systems. The origins of adult diseases, including elevated blood pressure are in childhood, and environmental controls in childhood may significantly reduce the risk of adult cardiovascular and neurovascular diseases. Therefore, identification of individual pollutants, pollution sources or mixtures that influence childhood blood pressure, cognition and growth is important for regulation and for child and future adult health.

Supplemental Keywords: Air pollution; child health; pregnancy; growth; blood pressure; cognition; inflammation; environmental justice; vulnerability; susceptibility

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1. OBJECTIVES

1.1. Hypotheses and Main Aims: Gestation and the first year of life are critical periods for somatic, cardiovascular, and brain growth. Toxic exposures during those periods may have life-long consequences.1-3 While a growing literature suggests that prenatal exposures to air pollution may reduce birth weight, the implication of this finding for later child development is unknown.

We hypothesize that gestational prenatal exposures to air pollution (individual pollutants, sources, or pollutant mixtures) will lead to dysregulation of somatic growth, increased blood pressure and reduced cognition in the first year of life. These adverse prenatal pollution effects will endure and track throughout childhood. Compounding the effects of prenatal exposures will be additional adverse effects of higher levels of infant, lifetime postnatal, and (for blood pressure) short-term exposures. Effect estimates will vary by individual pollutants, source type, and pollutant mixtures. Increased vulnerability or susceptibility to pollution effects on these adverse health outcomes will result from socioeconomic disparities, stress and violence, environmental tobacco smoke, and reduced maternal and child omega-3 fatty acid intake.

We will test these hypotheses using Project Viva, a unique ongoing pre-birth cohort of over 1,300 children with longitudinal repeated measures of somatic growth, blood pressure, and cognition. To test the Barker hypothesis4 that the origins of childhood chronic disease stem from prenatal as well as early-life postnatal exposures, Project Viva families were recruited between 1999 and 2002, during the first trimester of pregnancy. Potential sources of vulnerability and susceptibility to pollution (e.g., nutrition, tobacco smoke, stress and violence) were measured in the prenatal as well as the postnatal periods. Our Primary Aims are to determine the health effects of prenatal and postnatal exposures to individual pollutants, sources, and pollutant mixtures on:

Somatic growth, focusing on the following outcomes: (i) Birth weight, birth weight adjusted for gestational age (i.e., fetal growth); and (ii) Weight-for-length and change in weight-for-length (birth through to age 2); body-mass index (BMI) (kg/m2) and change in BMI (2 yr through 9 yr of age) (Aim 1);

Cardiovascular risk, focusing on the following outcomes: (i) Blood pressure (birth, 6 months, 3 years and 7 years) (primary outcome); and (ii) Cardiovascular fitness assessed by Step Testing (7 years) (Aim 2); and

Cognition, assessing visual memory at 6 months, 3 years and 7 years; language at 3 years and 7 years; intelligence at 7 years; and behavior at 7 years (Aim 3).

Finally, we aim to evaluate how the impact of pollution on somatic growth, cardiovascular risk and cognition is modified by measures of vulnerability and susceptibility, including: (i) Socioeconomic disparities, race/ethnicity; (ii) Maternal/family stress, violence and counter-balancing sources of community support; (iii) Environmental tobacco smoke and sources of indoor pollution; and (iv) Maternal prenatal, umbilical cord blood, and postnatal levels of omega-3 and n-6 fatty acids measured as nutritional intake and as blood/RBC levels (Aim 4).

Chronic systemic inflammation is a well-documented risk factor for high blood pressure and atherosclerosis,5 and dysregulation of growth.6, 7 Studies consistently show short-term pollution effects on increased systemic innate inflammation (e.g., IL-6, CRP) in vulnerable (elderly, diabetic, obese) adults.8-11 In contrast, studies do not consistently show associations of air pollution exposure with increased risk of developing allergy or allergic inflammation. Therefore,

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we also hypothesize that pollution will lead to increased innate and T helper pro-inflammatory cytokine production at birth, and to increased proinflammatory adipokines (e.g., obesity- and diabetes-associated leptin) in later childhood. We posit that pollution is more likely to upregulate innate and Th1 inflammatory mechanisms than to cause allergy. Thus our Secondary Aim is to explore the effects of long-term prenatal and postnatal, and short-term exposures to individual pollutants, sources, and mixtures on intermediate immune and endocrine responses, including: (i) Cord blood mononuclear cell (CBMC) lymphoproliferative responses, and innate (IL-6, TNF-α), adaptive Th1 (IFN-γ), and Th2 (IL-13) CBMC responses to stimulation with the mitogen PHA, Der f 1 (dust mite), and Bla g 2 (cockroach); (ii) Allergic sensitization (3 and 7 years); and (iii) Inflammation-related adipokines (leptin and adiponectin) levels (birth, 3 and 7 yr).

This Project is an essential component of our Center, whose goal is to assess life-stage-specific effects of pollution from cradle (Viva, Project 4; Project 5) through middle age (Framingham, Project 3) to old age (NAS, Project 2; Project 5). Animal models (Project 1) will help all 5 epidemiologic Projects refine their hypotheses, by giving further insight into how specific individual pollutants and mixtures influence cardiovascular and cerebrovascular function. Project 5 complements Project 4 by increasing our statistical power to investigate how trimester-specific effects of pollution on birth weight and fetal growth (weight for gestational age) differ by pollution mixture and by individual and neighborhood block level socio-economic status. The Exposure and Biostatistics Cores will provide measurements and estimates of pollution exposure including: individual pollutants (e.g. PM2.5 mass, number, elements, ions, BC, EC and OC, as well as PM10 and PM10-25, O3 and VOCs); pollution sources (e.g., traffic, power plants, soil and wood burning); and multi-pollutant mixtures. 1.2. Background:

Figure 1: Hypothesized effects of prenatal, long-term postnatal, and short-term postnatal air pollution exposures on child health in Project Viva

Overview: Considerable recent research has focused on the concept of ‘programming’, whereby a stimulus or insult applied at a critical or sensitive stage of development may have a long-lasting or permanent effect on the structure or function of the organism.4 Brain development is rapid in the last trimester and in the first 6 months of life.12,13 The placenta, cardiovascular system and lung develop in phases that are trimester-specific. Alveolar development extends beyond birth.14,15 Maternal cigarette smoke, a mixture of particles and gases,16 has been demonstrated to adversely influence placental blood flow, fetal development17 and birth weight, with somatic, cardiovascular, pulmonary18 and neurocognitive effects that may last into later childhood.16 We posit that exposures to pollution during critical early-life time windows will also have long-lasting effects on child growth, cardiovascular function or cognition, and that cumulative

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exposure or short-term exposures after birth have additional adverse effects on these outcomes (Figure 1).

1.2.1. Ambient pollution, birth weight, and somatic growth (Aim 1): In the mouse model, urban air pollution reduced fetal weight despite attempts to improve diffusive transport across the placenta by increasing fetal capillary surfaces.19 Accumulating human epidemiologic evidence has linked traffic and industrial air pollution sources to perinatal mortality,20 low birth weight,21 small for gestational age,22 and preterm birth.23 Preterm birth rates decreased during the Utah Valley Steel Mill closures.23 In a recent review, Ritz and Wilheim2 noted the limitations of risk ascertainment using record-based administrative data (e.g. birth certificates), and advocated for further evaluation of residual confounding by socioeconomic status (SES) using data with more detailed information on additional risk factors for these outcomes. Low birth weight itself has been linked to impaired cognition, elevated blood pressure/hypertension,24,25 and dysregulation of somatic growth (including insulin resistance, diabetes,25 and central obesity25, 26) in later childhood. Both reduced height and increased weight have been associated with maternal smoking during pregnancy.27-34 There are no prospective studies assessing chronic adverse effects of prenatal and infant exposures to air pollution on the trajectory and tracking of growth throughout childhood.

Previous work: We showed an increase in infant mortality with increased pollution in Mexico City.35 In Eastern Massachusetts (1996-2002) we found independent effects of traffic and of individual and area-based measures of SES on the risk of low birth weight and low weight-for-gestational-age.36 Showing the importance of choice of statistical methodology in estimating exposure, we found that elevated traffic pollution, particularly in the third trimester of pregnancy, predicted low birth weight in the greater Boston area.37 We again showed trimester specific effects that varied by pollutant (NO2, CO, PM10 and PM2.5) and by race, in a study of 358,504 births in Massachusetts and Connecticut from 1999 to 2002.38

1.2.2. Ambient pollution and elevated blood pressure (Aim 2): The origins of atherosclerosis and hypertension are in childhood.39 However no study has evaluated the relation of pre- or postnatal air pollution exposure to blood pressure levels in childhood. A relatively small but accumulating epidemiologic and controlled human exposure literature demonstrated air pollution effects on vasoconstriction of conduit arteries,40 brachial artery endothelial dysfunction41,42and elevation in diastolic and systolic blood pressure43,44 in adults.

Previous work: In Boston cardiac rehabilitation patients, our group demonstrated an increase in diastolic and mean arterial blood pressure with increases in PM2.5 averaged over the previous 5 days.45 With EPA STAR and Center support, we collaborated with Drs. Brook and Urch in the replication of the earlier human chamber findings of associations of elevated particulate pollution with elevated blood pressure.41 We have also documented increases in blood pressure (systolic and diastolic) and decreases in conduit (brachial) artery diameter with increases in organic carbon levels in a repeated measures study of people with diabetes (https://www.isee2009.ie). In all of these studies the level of increase has been in the clinically relevant range of 2-5 mm Hg.46 In the Normative Aging Study, we showed that elevation of estimated short-term home BC is associated with postural blood pressure changes,47 and elevation of long-term BC exposure is associated with higher blood pressure. Mechanisms for the CAPs-related blood pressure changes were evaluated by our Center.48 In dogs, increased baroreceptor reflex sensitivity appeared to

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partially compensate for particle-induced alterations in blood pressure and alpha-adrenergic blockade ablated CAPs responses.

1.2.3. Ambient pollution and cognition (Aim 3): While both prenatal maternal smoking and air pollution exposure have been hypothesized to influence cognitive development in children, the data supporting this hypothesis are sparse, and potentially confounded by other environmental factors associated with socioeconomic disadvantage.49 Maternal smoking during pregnancy was associated with a decrease in children’s global cognitive scores as well as in scores of verbal, quantitative, and executive function at 4 years of age.50 In 226 urban children prenatal ETS exposure predicted a 5 point decrement in the Bayley MDI scores. Early-life exposure to indoor gas stove emission/NO2 was associated with reduced cognitive function and inattention symptoms at age 4.51 Particles have been shown to translocate from the nose up the olfactory nerve into the brain, including the striatum frontal cortex, and cerebellum.52,53 This in turn is associated with increased brain inflammatory responses54,55 and changes in neurotransmitter levels.56 In humans, diesel exhaust exposure has been shown to alter EEG responses, with patterns indicative of cortical stress.57 In polluted areas of Mexico City, dogs had more prefrontal lesions, neuroinflammation, gliosis, and particle deposition. In these polluted areas, brain MRI’s of children had more prefrontal lesions; autopsy studies of accident victims showed upregulation of cyclooxygenase-2 and CD14.58-60

Previous work: We found that higher levels of black carbon predicted decreased cognitive function (verbal and nonverbal intelligence, and memory) in a cross sectional study of children of a mean age of 9 yrs.61 Fish is a source of both mercury and omega-3 fatty acids. In Project Viva, higher fish intake in pregnancy was associated with better infant cognition (visual recognition memory) and with better cognition at age 3 (Peabody Picture Vocabulary Test and Wide Range Assessment of Visual Motor Abilities), whereas higher mercury levels were associated with lower cognition.62,63

1.2.4. Effect modification by socioeconomic disparities and stress, environmental tobacco smoke and sources of indoor pollution, maternal prenatal and cord blood omega-3 and n-6 fatty acid levels (Aim 4):

Maternal stress, ethnicity, and socioeconomic disparities: Exposure to violence modified the effects of estimated lifetime exposure to traffic pollution on risk of asthma in an East Boston cohort.64 In a Boston birth cohort we showed that increased maternal stress predicted an increase in innate immune responses (elevated TNF-α), IgE levels, and risk of wheeze in young children.65,66 Among inner city school children we found that community violence predicted increased asthma morbidity.67 We have measured maternal stress, violence, and family support during and after pregnancy, and have preliminary data relating stress to reduced production of IL-10, an anti-inflammatory cytokine. We have shown how socioeconomic disparities adversely influence pediatric health.68-77

Environmental tobacco smoke and indoor pollution sources: For over twenty years we have contributed to the evidence that prenatal maternal tobacco smoke, a source of oxidative stress and placental vascular constriction, contributes to the risk of low birth weight, changes in neonatal immune responses, reduced airflow in infants, and wheeze and asthma in young children.18,78 We have also shown that ETS exposures after birth increase the risk of wheeze, asthma, and reduced lung function.79-82 We have demonstrated adverse effects of gas stoves on respiratory infection and wheeze.83

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Omega-3 fatty acids: In Project Viva we found direct protective effects of prenatal ingestion of fish/n-3(omega-3) fatty acids on growth84 and cognition in children;63 on reduced preeclampsia risk in the mothers;84 and on birth immune outcomes.85 In addition to our study, a large literature supports primary fatty acid effects on growth, cardiovascular12,86 and cognitive outcome. One intervention study suggests that omega-3 fatty acids may reduce pollution effects on cardiac autonomic outcomes in the elderly.87

1.2.5. Immune (allergic and innate) and endocrine responses: Season of birth had the greatest influence on innate and adaptive cytokine responses to innate and adaptive stimulants in our urban multi-city URECA birth cohort.88 We have also demonstrated clustering of childhood early-life maternal allergic sensitization74 in central urban Boston, with higher rates in the inner city than in suburban areas, and with higher rates among African American study participants.89-

92 Maternal smoking during pregnancy, black race/ethnicity, and inadequate or excessive maternal weight gain in pregnancy were associated with increased lymphoproliferative responses to cord blood mononuclear cell stimulation with inhalant allergens (Bla g 2/cockroach or Der f 1/dust mite) in our Project Viva cohort.80,93 We have shown a heightened pro-inflammatory response to air pollution (elevated IL-6, C-reactive protein and white blood cell count) in elders with diabetes or obesity.9 In a mouse model of diet-induced obesity, PM2.5 exaggerated adipose inflammation and insulin resistance.94 Maternal smoking in pregnancy has been associated with increases in the cord blood leptin, but prenatal air pollution effects on this pro-inflammatory adipokine are unknown.95 A cross-sectional analysis of clinic and administrative data from Ontario showed increased diabetes rates for adults in higher estimated NO2 areas, stimulating the hypothesis that air pollution might not only worsen inflammation in those with diabetes, but it might also increase the risk of diabetes.96

1.2.6. Evidence for variability of exposure: The spatial distribution of our population is illustrated in Figure 2. Detailed evidence for temporal and spatial variability for the prenatal, lifetime exposure postnatal and short-term exposures to individual pollutants, sources and mixtures are presented in the Exposure Core. We have previously demonstrated sufficient temporal and, for estimated BC, spatial variability of individual particle and gas pollutants to evaluate their health effects in Boston area studies of short-term pollution effects (Sections 1.2.1-1.2.4). In Table 1 we show temporal variation in trimester-specific averages of individual pollutants in Boston (2003-2008) that is at least as great as the trimester-specific variation associated with low birth weight in studies by Bell and others (Section 1.2.1). Distinct seasonal patterns of regional pollution and small changes in annual levels are responsible for the trimester-specific pollution variation, which we expect to be similar in 1999-2002.

Table 1: Interquartile range of 3- and 6-month PM2.5 BC and O3 concentrations i n Boston

Pollutant Percentile

PM2.5*

(25th)

PM2.5

(75th)

BC** (25th)

BC (75th)

O3† (25th)

O3 (75th)

3 mo 8.4 10.8 570 734 18.5 30.2 6 mo 8.9 10.2 589 710 20.1 28.7

* PM2.5(μg/m3); ** BC(ng/m3); †O3 (ppb)

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Figure 2: Enrollment distribution and current distribution of our population

Summary: While there is consistent evidence linking prenatal pollution exposures (particles and gases) to low birth weight, no prior studies have evaluated the effects of prenatal or infant exposures on growth throughout childhood (1.2.1.). While consistent evidence links short-term exposures to adverse cardiac outcomes in adults, no studies have explored the chronic or acute effects of air pollution on blood pressure in young children (1.2.2.). Some animal and cross-sectional human studies suggest that pollution may influence cognition and behavior. This merits follow-up in prospective pre-birth cohort studies like Project Viva (1.2.3.). Factors that may modify pollution effects include stress, violence, socioeconomic status, maternal smoking, indoor air pollution sources, as well as fatty acids and their nutritional sources (1.2.4.). We have demonstrated strong evidence for variability of exposures to individual pollutants (1.2.5.). The proposed Center would enable us to estimate exposures to individual pollutants, sources and mixtures during the following life periods: 1) gestation; 2) the vulnerable period of additional brain, vascular and lung development in the first year of life; and, 3) over the entire lifetime of Project Viva children. A prospective pre-birth cohort study such as ours offers a unique opportunity to evaluate long-term and short-term effects of pollution on growth, blood pressure and cognition. 2. APPROACH

2.1. Project Viva Study Design: 2.1.1. Overview: From 1999 through 2002, pregnant women were invited to participate in the study at their initial prenatal visit to one of 8 urban and suburban practices of Harvard Vanguard Medical Associates, a multispecialty group practice. We performed in-person evaluations during the first and second trimester, after the birth of the child, and at 6 months, 3 years, and 7 years after birth. We have followed the family, mother and child on a semiannual or annual basis by questionnaire, and also by medical record review since birth. Age 7 in-person visits are in progress; we expect to complete ~1,300 visits (>800 are already completed); ~1,400 children are followed by questionnaire. External funds support measurement of all health outcome data for the primary and secondary aims of our Center proposal. Project Viva is currently funded through: 2RO1 HD034568-06A2; 5RO1 HL064925-07; 5RO1 ES16314; and 1RC1HD063590-01.

Enrollment 2009

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2.1.2. Baseline (Visit 1): At the time of enrollment, we collected baseline questionnaire data on reported height and pre-pregnancy weight, demographics, socioeconomic status, detailed diet using the validated food frequency questionnaire,(FFQ)97 health habits, medical and family history, reproductive history, social support, psychosocial factors, and racial discrimination.

2.1.3. Mid-pregnancy follow-up (Visit 2): At 26-28 weeks’ gestation, participants completed a second FFQ, a physical activity frequency questionnaire, and a questionnaire updating health habits, including a section on interpersonal violence and depression.

2.1.4. Blood sampling during pregnancy and at delivery; measurement of nutritional and toxic exposures; immune and epigenetic outcomes; DNA: At prenatal Visits 1 and 2, we collected maternal blood and stored plasma, erythrocytes, and DNA in liquid nitrogen freezers. We also collected and stored 1,022 venous umbilical cord blood specimens in aliquots of serum and plasma. We extracted DNA from the cord blood buffy coat and from peripheral blood for children without cord blood specimens. Maternal and cord blood n-3 and n-6 fatty acid levels and maternal Hg levels were measured on stored samples.93, 98 In 427 newborns fresh cord blood mononuclear cells were isolated and stimulated with 5 μg/ml mitogen phytohemagglutinin (PHA), or the allergens 30 μg/ml Der f 1 (dust mite) or 30μg/ml Bla g 2 (cockroach).80, 93 Lymphocyte proliferation was measured and supernatant was harvested at 24-h for innate cytokines (IL-6, TNF-α) measurement and at 72 h for Th cytokines (IFN-γ, IL-13), IL-10 measurement as previously described.80, 93 Over the next 2 years we are funded to measure global DNA methylation in maternal and cord blood.

2.1.5. Postpartum follow-up visit; growth and blood pressure measures: After delivery, we visited the mother and newborn at the hospital. We administered a short questionnaire to the mother to update health habits during the pregnancy, including a short diet questionnaire focused on n-3 and trans-fatty acids, and we collected medical record information about the labor and delivery. For 2,127 infants, we obtained birth weight data from hospital records. We measured head, abdominal, and chest circumferences using a Lefkin woven tape, and length with a research standard length board. We obtained five blood pressure measurements, each one minute apart, using the Dinamap Pro100 automated oscillometric recorder.

2.1.6. Automated medical record system; longitudinal growth measurements: Medical data for mothers at baseline and throughout pregnancy are available from the HVMA EpicCare automated medical record system, as are prospective clinical growth data obtained at annual well-child visits on their children, as well as data from child sick visits. Approximately ¾ of the children remained with HVMA by age 3 and ~2/3 by age 7. For those who have left the HVMA practice, we retrieve longitudinal height and weight data from the pediatric providers. While not as accurate as those we measure at in-person visits at ages 3 and 7, these annual longitudinal growth measurements allow analyses using repeated outcome measures.

2.1.7. Six-month follow-up visit; growth, blood pressure, and cognition measures: At the in-person visit, we measured mother’s and child’s height/length, weight and blood pressure, using the Dinamap recorder, and documented conditions [order of readings, cuff size, limb, body position (sitting, semi-reclining, reclining, standing)], and, for the child, state (sleeping, quiet awake, active awake, crying). We recorded child blood pressure up to five times at 1-minute intervals. We had the mother complete a brief validated food frequency questionnaire, called PrimeScreen.99 We also administered a physical activity questionnaire, and asked the woman about barriers to physical activity, other health habits such as smoking, weight perception,

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functional status measures, employment, physical symptoms, and health status, including depression, using the validated Edinburgh postpartum depression scale100. We also collected questionnaire information about infant diet and environmental exposures, and wheeze/allergy symptoms. We measured the child’s cognitive development using the Visual Recognition Memory (VRM) evaluation.

2.1.8. 1-through 9-year annual questionnaire follow-up; exposure and outcome data: The annual maternal-completed mailed questionnaires updated parental social, economic, and demographic factors; neighborhood characteristics; maternal health habits and health status. We also queried indoor environmental exposures, focusing on indoor air pollution and allergen sources that might influence asthma and allergic disorders. Child dietary and activity questions were age-specific. At age 2 a validated FFQ was introduced. On an annual basis, from age 1 onward, we ascertained respiratory and allergy symptom and disease outcomes, including wheeze and asthma diagnosis, using standard questionnaire tools.101

2.1.9. 3-year follow-up visit: We collected the following measurements: 1) Anthropometry/ growth. On the child and mother we measured mid upper arm, waist, and hip circumferences, and subscapular and triceps skinfold thicknesses. We measured height and weight using a research standard stadiometer and scale; 2) Five blood pressure readings. On the child and mother, using the Dinamap Pro100 automated monitor, according to standard guidelines;102 3) A cognitive developmental assessment. On the child using the Peabody Picture Vocabulary Test-III and the WRAVMA (see Section 2.3 for details); and 4) A blood specimen/allergy testing. We obtained 12 cc of blood for lead screening, a complete blood count (including eosinophil count), and peripheral blood mononuclear cell stimulation and cytokine measurement. We stored aliquots of red and white cells and plasma for future use, and measured total IgE and allergy to food and indoor allergens.

2.1.10. 7-year follow-up visit: We are collecting the following measurements on the children: 1) Anthropometry/growth. We measure height and weight, mid upper arm, waist, and hip circumferences. subscapular and triceps skinfold thicknesses. We also use the Hologic Discovery A fan-beam DXA scanner (Hologic, Inc., Bedford, MA), measuring bone mineral density and body composition, including fat mass and fat-free mass; 2) Five blood pressure readings using the Dinamap Pro100;251,252 3) Cardiovascular fitness using the Step Test;103,104 4) A cognitive developmental assessment using the KBIT-2, WRAVMA drawing subtest, providing comparability to earlier visual memory tests, and the WRAML2 (Section 2.3); 5) Allergy and adipokines: We obtain blood and store aliquots of plasma, red and white blood cells. We are funded to measure allergy to 11 allergens: dust mite, cockroach, cat, dog, mixed grass pollen, mixed tree pollen, Alternaria tenuis, egg, milk, and peanut) using the UniCAP system (Pharmacia & Upjohn, Kalamazoo, MI), as well as leptin and adiponectin levels; and 6) Spirometry: We use the EasyOne Spirometer (ndd Medical Technologies, Andover, MA). After performing baseline spirometry according to ATS criteria, we evaluate bronchodilator responsiveness after subjects take two puffs (180 μg) of albuterol by a metered-dose inhaler.

2.2. The Main Exposure Variables (See Exposure and Biostatistics Cores): Project 4 will use the same six exposure metrics as in Projects 2, 3 and 5. These include short- and long-term exposures to individual pollutants, sources and mixtures. In addition, we will assess prenatal and infant exposures during critical windows of somatic, cardiovascular and brain development.

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2.2.1. Short-term Exposures to Individual Pollutants, Sources and Mixtures: The duration of the short-term exposure to be considered will range from a day to weeks, depending upon the health outcome. Short-term exposures will be estimated by averaging the corresponding daily concentrations of individual pollutants or the daily contributions of the identified pollution sources. We will assess daily exposures to the following individual pollutants: PM2.5 mass, particle number, elements, ions, BC, EC and OC, as well as PM10 and PM10-2.5, O3, and VOCs using data from the Boston HSPH Supersite or state and local monitoring sites. We will also estimate participant home-specific daily exposures to PM2.5, O3 and BC using satellite spatial data for PM2.5 (available since 2001), and spatiotemporal models for O3 and BC (available since 1995). The PMF EPA source apportionment method will be applied to the Supersite data to estimate exposure to daily contributions of individual PM sources. Therefore, estimated PM2.5, BC and O3 levels will be temporally and spatially-resolved. All other pollutant and source exposures will be temporally, but not spatially-resolved, as they will be based on Supersite data. Finally, Supersite data will also be used to assess the relative toxicity of short-term exposures to the different pollutant mixtures types as described in the Exposure and Biostatistics Cores. Specifically, we investigate the effects of PM2.5 mass where “mixture type” will be used as an effect modifier. This may lead to the identification of multi-pollutant mixtures that are relatively more toxic.

2.2.2. Trimester-specific prenatal exposures; exposures in the first 6 months of life: We will estimate exposures during these critical windows of development by averaging daily pollutant concentrations (or source contributions) over the 90- and 180-day periods of interest. As our cohort was in utero or under 1 year of age in 1999-2003, we will use filters from this period to measure elements and estimate source contributions by PMF. As outlined in the Exposure and Biostatistics Cores and enumerated in more detail in the next section, we will apply a spatial clustering technique that groups zip code areas according to their long term average mixture profiles. We will then examine whether this area-based grouping modifies the effects of exposures to individual pollutants during specific trimesters and the first 6 months of life.

2.2.3. Long-term and Lifetime Exposures: We will look at long-term exposure using a one-year window. In addition, for the age 3 and 7 primary outcomes, we will also estimate lifetime exposures, averaging over the first 3 and the first 7 years of life, respectively, and taking into account changes in home address because of moving. Estimates of exposures to individual pollutants, sources and mixtures will be obtained as follows:

1) We will assess spatially-resolved long-term exposures to individual pollutants by averaging daily concentrations for PM2.5, O3 and BC. Spatially resolved exposures for these pollutants will be obtained as follows: i) PM2.5 exposures will be estimated using MODIS and GOES satellite data starting in 2001; ii) BC and O3 exposures will be assessed using spatiotemporal models starting in 1995; and iii) seasonal averages of the remaining pollutants, including fine particle EC, OC and elements, as well as VOCs, NO2 and PM10-2.5 will be estimated using long-term spatial models based on newly collected monitoring data proposed as part of the Center;

2) Long-term exposures to sources will be determined using data from the spatial sites which will be analyzed using PMF. Subsequently, for each source type, we will use all data from all sites to spatially smooth seasonal source contributions using a method presented in the Biostatistics Core. These models will yield estimated geo-coded seasonal exposures to each of the identified sources in the region; and

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3) Using the estimated long-term exposures to the individual pollutants (pollutant-specific spatial surfaces), we will calculate the average pollutant concentrations for each zip code in the Region. Subsequently, cluster analysis will be performed to group the different zip codes according to their average multi-pollutant concentration profiles. This spatial clustering will enable us to identify zip code areas within the study domain with similar pollution mixtures. Once the cluster types are identified and characterized, we will use mixture type for each zip code as an effect modifier for the health risks associated with long-term exposure to PM2.5.

2.3. The Main Outcome Variables:

2.3.1. Weight-for-length, body-mass index (kg/m2) (BMI): Weight, length, height and weight are collected prospectively through annual well-child pediatrician visit records and through direct study measurement at birth, 6 months, 3 years and 7 years. To account for gender-specific growth with age, in longitudinal analyses, birth weight for gestational age, weight for length, and BMI will be considered as z-scores (http://www.cdc.gov/nccdphp/dnpa/growthcharts).105 Outcomes will be represented categorically (e.g., above versus below 85th percentile) or continuously as appropriate. Growth will also be evaluated as annual change in weight for length up through age 1, and as change in BMI from age 1 through age 9.

2.3.2. Blood Pressure: Systolic and diastolic blood pressure measured at birth, 6 months, 3 years, and 7 years are treated as continuous outcome variables. To assess associations between predictors and blood pressure, we have used mixed models that incorporate each of the up to 5 blood pressure measurements from each infant as repeated outcome measures. An advantage of this modeling approach, compared with using the average of available measures for each child as the outcome, is that individuals with more measurements and less variability among those measurements get more weight than those with fewer measurements and/or more variability.

2.3.3. Cognitive and Behavioral outcomes:

Visual Recognition Memory (VRM)(Age 6 mo).106 The VRM test reflects the infant’s ability to encode a stimulus into memory, to recognize that stimulus, and to look preferentially at a novel stimulus. The VRM score in infancy predicts intelligence in childhood and early adolescence as strongly as other standardized tests of infant development.

The Peabody Picture Vocabulary Test (PPVT) (Age 3).107 The PPVT evaluates receptive vocabulary. The PPVT generates raw scores which are converted to standardized scores for children aged 2½ years and older, based on a nationally stratified sample of children and adolescents. Scores on the PPVT are strongly correlated (r>=0.90) with verbal and full scale IQ on longer instruments such as the WISC-III.107

Wide Range Assessment of Visual Motor Ability (WRAVMA) (Age 3). It evaluates three domains of visual motor development: visual spatial (matching test), visual-motor (drawing test), and fine motor skills (pegboard test), which are used to generate a total standard score.108 This test has norms for children ages 3 years and older and is moderately correlated with IQ (r-0.60), and is sensitive to neurotoxicants such as lead.

Kaufman Brief Intelligence Test (KBIT-2) (Age 7).109 The KBIT verbal subscale and the PPVT are sufficiently similar to enable comparison between age 3-PPVT and age 7-KBIT responses. The KBIT-2 measures two distinct cognitive functions through two subtests: Verbal and Nonverbal. The Verbal subtest contains two item types (Verbal Knowledge and Riddles) that measure crystallized ability; the Nonverbal subtest includes a Matrices subtest that measures

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fluid reasoning. The two subtests are used to generate an IQ Composite. The KBIT correlates strongly with full length measures of intelligence such as the WISC-III and WISC-R.110,111  

Wide Range Assessment of Memory Learning (WRAML2) (Age 7).112 The WRAML2 is an individually administered test battery designed to assess memory ability. We have chosen to include the Picture Memory and Design Memory subtests here, which comprise the Visual Memory Index, as we have previously observed associations of fish consumption and mercury exposure with the same domain (using Visual Recognition Memory testing) among a subset of Project Viva infants evaluated at 6 months.63 All subtests have scaled scores with a mean of 10 and standard deviation of 3 and the index standard score has a mean of 100 and SD of 15.

Strengths and Difficulties Questionnaire (SDQ) (Age 7).113 The SDQ is a brief behavioral screening questionnaire that can be completed in 5 minutes by the parents or teachers of children aged 4 to 16. The SDQ asks about 25 attributes, some positive and the others negative; they are divided into 5 scales, 4 of which added together generate a total difficulties score. The SDQ's emphasis on strengths as well as weaknesses makes it particularly acceptable to community samples.114 Scores from the SDQ are highly correlated with those on the longer Child Behavioral Checklist (CBCL).114 The SDQ was significantly better than the CBCL at detecting inattention and hyperactivity, and at least as good at detecting internalizing and externalizing problems. Normative data are available from administration to almost 10,000 US children in the National Health Interview Survey.115 We survey both parents and teachers using the SDQ.116

Behavior Rating Inventory of Executive Function (BRIEF) (Age 7).117 The BRIEF consists of two rating forms—a Parent questionnaire and a Teacher questionnaire—designed to assess executive functioning in the home and school environments. These theoretically and statistically derived scales form two broader Indexes: Behavioral Regulation (three scales) and Metacognition (five scales), as well as a Global Executive Composite score. Factor analytic studies and structural equation modeling provide support for the two-factor model of executive functioning as encompassed by the two Indexes. Validity scales measure Negativity and Inconsistency of responses. The BRIEF is used in evaluating children ages 5 to 18 years. Standard scores have a mean of 50 and a standard deviation of 10.

2.4. Variables considered as sources of vulnerability, susceptibility and confounding: Our longitudinal data on social/psychosocial factors that may modulate pollution effects on child cardiovascular or cognitive outcomes is rich. We also have prospective repeated measures data on parental education, race/ethnicity, census-based neighborhood wealth, and maternal depression.100 We have prenatal data on social support, additional psychosocial factors, and racial discrimination, and interpersonal violence (Sections 2.1.2 and 2.1.7). While most of these variables are categorical, some are scores. The categorical variables maternal and household tobacco smoking and sources of indoor pollution (e.g., gas stoves) have been ascertained on an annual basis by questionnaire. Fatty acid diet measured by FFQ and maternal prenatal/cord fatty acid levels will be considered as modifiers of pollution effects on cognition and blood pressure.87 Measures include total n-3 fatty acids (ALA, EPA, DHA), total n-6 fatty acids (LA, AA), trans fatty acids (total 18:1 and total 18:2), and the n-6:n-3 ratio estimated by diet and measured in maternal RBCs and cord plasma. Blood fatty acid concentrations are reported as area percentages of total fatty acids measured. These factors can be considered as potential confounders, as well as effect modifiers. Additional potential behavioral and dietary confounders will be considered, based on our previous extensive work documenting other independent predictors of growth (e.g.,

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maternal diet and weight gain in pregnancy,118 child intake of sugar-sweetened beverages, fast food, and TV watching119), blood pressure (e.g., prenatal calcium and iron106,120) and cognition (e.g., prenatal fish and mercury) in this cohort.63,118,121-123 2.5. Analytic Plan for Specific Aims:

2.5.1. Overview: The primary responses of interest include somatic growth (Aim 1); blood pressure (Aim 2), and cognition (Aim 3) in infancy and up through age 7 (or age 9, in the case of somatic growth). Primary exposures (individual pollutants and sources) are summarized in Section 2.4. Primary exposure periods of interest include the first, second and third trimester of gestation; the first 6 months of life, lifetime exposures up to ages 3 and 7, and (for blood pressure) short-term exposures. We are interested in whether prenatal and infant exposures have effects in infancy, and whether the trajectory of growth, blood pressure, or cognition is permanently influenced by early life exposures. Finally, we want to know whether long-term (lifetime) exposures beyond infancy or short term-exposures compound the effects of pollution in early life. In what follows we outline our approach to estimate main effects of individual pollutants/sources and each outcome. Using interaction terms, mixtures/clusters will be considered as effect modifiers, as will sources of susceptibility and vulnerability (Aim 4), using the same class of models as those outlined below. 2.5.2 Aim 1 (Growth): We will use growth curve analysis methods to analyze the association between growth and individual pollutants, sources and mixtures. These models are longitudinal data models that allow individuals to have subject-specific rates of growth. Let Yij denote a growth outcome (e.g. weight-for-length or BMI z-score, change in BMI, birth weight for gestational age, etc.), and let polli denote an exposure metric of interest (we outline the specific choices for this covariate in 2.2). We will use as a starting model a quadratic growth curve model, but will explore the adequacy of this assumed functional form for growth. This baseline model is:

Yij = (β0 + ui) + (β1 + vi)ageij + (β2 + wi)ageij2 + β3 polli + β4 polli * ageij + β5 polli * ageij

2 + ...+εij . (1)

The ui , vi, wi are subject specific random effects, and we will evaluate whether all three of these terms are necessary for our data. The hypotheses of interest corresponds to a test of H0 :β3 = β4 = β5 = 0 . In the case that the quadratic model does not fit the observed growth trajectories well, we will also consider nonparametric growth curve models, which fall within the class of generalized additive mixed models (see Section 3.1.4 of the Biostatistics Core).

Yij = fei(ageij )+ ui + ....+εij (2)

where i e= 0,1, or 2 is a categorical exposure indicator defined by the tertile of a given pollutant. This assumes different growth patterns for subjects having exposures in different tertiles of exposure, and interest focuses on whether H0 = f0 = f1 = f2 , which can be tested using the difference of model deviances. We will also consider extensions of this model that allow the functional form of f to vary smoothly with a continuous exposure, which can be accomplished by assuming that the coefficients in a basis representation for f vary smoothly with the continuous exposure. This will avoid loss of power due to categorization of continuous exposure.  Primary exposure hypotheses for growth will focus on pre-natal exposures, by trimester, and on exposure in the first 6 months of life. As noted in 2.2, associations with PM2.5, O3 and BC will be estimated using both temporal and spatial variation in estimated trimester-specific and 6-month

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exposures. Associations with other individual pollutants and sources, and mixtures will be estimated using temporal variability in the Supersite measurements.   2.5.3. Aim 2 (Cardiovascular Risk): We will conduct longitudinal regression analyses of the association between blood pressure (both diastolic and systolic) at birth, at 6 months, year 3 and year 7, and the trend in blood pressure from birth to 7 years. These analyses will use linear mixed models similar to those presented in the previous sub-section. Care will be taken to check the model assumptions for the form of BP trajectory, and we will consider the more flexible nonparametric growth curve models outlined above if necessary. Primary analyses will again focus on prenatal and first 6 months of life exposure, and we will use the same exposure covariates as described above for somatic growth. We will also consider two additional potentially important time windows of exposure for this outcome. We will consider cumulative lifetime exposure from the time of birth up to the time of BP measurement. For this metric, the model takes the slightly different form: Yij = (β0 + ui) + (β1 + vi)ageij + (β2 + wi)ageij

2 + β3 pollij + ...+εij (3)

where here pollij denotes the cumulative lifetime exposure for subject i at measurement j. This slightly different formulation reflects the fact that the exposure metric is time-varying, rather than subject-specific. Second, we will consider the acute effects of short-term exposures. These assessments will use model form (3), but with short-term exposures (on the order of daily to weekly) calculated from either the central Supersite, spatio-temporal models or satellite data depending on the pollutant (see 2.2). Finally, in order to investigate whether lifetime exposures have an effect beyond that from exposures during early developmental periods, we will consider multi-exposure models that contain both prenatal (or 6 month exposure) and lifetime exposure.

2.5.4. Aim 3 (Cognition): We will first assess associations between pollution exposures and cognition at 6 months and at 7 years of age using linear regression models that use cognitive testing score as the outcome and an exposure metric and confounders as independent variables. For the 6 months outcomes, separate models will be run using estimated average exposure (individual pollutants or source contributions; see section 2.2) for each trimester and for the first 6 months of life. For outcomes judged to be comparable between ages, we will also consider longitudinal regression analyses (2.5.3). We will assess effect modification of PM2.5 by pollutant mixture, with pollutant mixture defined at the level of zip code. For the 7 year outcomes, single-exposure models will include those that use trimester-specific, first 6 months, and 7 year lifetime exposures. We will also consider multi-exposure models that contain both an early (trimester-specific or first 6 months) and lifetime exposure.

2.5.5. Power: For the study design and sample size (1,300 subjects) proposed in this Viva cohort, we estimated our power to detect associations between one of our primary exposures, spatially-resolved estimated BC concentrations during gestation, and both blood pressure (longitudinally) and cognition (at year 7). For these calculations, we used existing Boston-area exposure data, assuming that the SD of gestational BC exposure is 0.3. For blood pressure, we assumed a longitudinal design (birth, 6 months, 3 years and 7 years) and that blood pressure exhibits a within-subject longitudinal correlation of 0.5. Under these assumptions, we estimate that we will have 80% power to detect an effect size in blood pressure of 1.4 mm per IQR increase in gestational BC exposure, which in the range of effect sizes reported by our group.124 For cognition, we base our power estimates on preliminary data for the WRAML General Index generated as part of our Center.61 We estimate that we will have 80% power to detect a

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decrement in WRAML score of -2.0 per IQR increase in log BC concentration, which is a smaller effect than that of -3.7 reported by Suglia et al (2008). Therefore, we anticipate that this study will be more than amply powered to detect effect sizes of scientific interest for these outcomes. As there has been some literature reporting the effects of maternal smoking on growth, but no literature on pollution and growth, we estimate the minimum effect size of a pollution effect, expressed as a percentage of recently reported smoking effect (Cardiff et al. 1987). Assuming a linear growth trend for simplicity, we estimate that we will have 80% power to detect a pollution effect (defined by an interquartile increase in pollution concentration) approximately 13% that reported for smoking. 3. EXPECTED RESULTS:

3.1. Direct Public Health Benefits: Elevated blood pressure, reduced cognition, behavioral problems, and abnormal somatic growth are significant burdens on individuals, their families and society. Beginning at 115/75 mm Hg, cardiovascular risk doubles for each increment of 10/10 mm Hg. Blood pressure rises throughout life. The higher the blood pressure, the higher the risk of myocardial infarction, stroke, cardiac failure and kidney failure.125 Cognitive deficits and child behavior problems not only impose costs and burdens on children and their families, but also on their school systems.126,127 The origins of adult diseases, including elevated blood pressure are in childhood, and environmental controls in childhood may significantly reduce the risk of adult cardiovascular and neurovascular diseases. If we define individual pollutants, pollution sources or mixtures that influence childhood blood pressure, cognition and growth, this will have importance for regulation and adult, as well as child health.

3.2. Additional Opportunities for Research relevant to Public Health: Assessing prenatal (by trimester), cumulative postnatal, and short-term childhood exposures to air pollution for the Project Viva participants will also create important opportunities, as funds external to this proposal become available, to evaluate pollution effects on: 1) development of asthma, wheeze, and airflow obstruction by spirometry; and 2) epigenetic responses (e.g., DNA methylation in pregnant mothers and in newborns). Furthermore, opportunities will also be created to evaluate modification of pollution effects by additional sources of vulnerability and susceptibility, including prenatal maternal or postnatal child antioxidants; common polymorphisms influencing oxidative stress; and home allergens.

While measurement of the outcomes for these opportunities is already partially completed or funded, we are seeking additional funds from sources external to the EPA Center for additional data management and data analysis support, and for additional longitudinal measurement of these costly and complex outcomes (e.g., DNA global at age 7; gene-specific methylation at birth and age 7), and for further pulmonary function and symptom follow-up for the children beyond age 9. However, this EPA Center is absolutely essential for measurement and estimation of pollution exposures.

3.3. Background Evidence for Importance of Additional Opportunities:

Asthma: There is strong and consistent evidence from our group128-133 and many other groups world wide,134,135 that multiple ambient pollutants worsen wheeze and lung function in children and adults with established asthma. Animal studies136 and human137 inhalation and epidemiologic studies provide some evidence that air pollution may cause allergy or asthma development. However, findings are still sparse and inconsistent in birth cohort and other epidemiologic

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studies.138-143 It is not certain whether air pollution is contributing to the resurgence or persistence of airflow obstruction, or whether it is “causing” asthma development, particularly in longitudinal studies of pollution and asthma development that begin at school-age.

Epigenetic Responses: We have also shown rapid DNA methylation associations with elevated levels of traffic pollution and with work in a foundry. Pollution-related changes were found in oxidative stress pathway-related to iNOs methylation.144 In Project Viva we are funded to measure cord blood global DNA methylation, and are seeking external funding to measure methylation at age 7, and candidate oxidative stress genes. This would create the opportunity to evaluate pollution effects on methylation and effect modification by oxidative stress genes.

Antioxidant Protection/Oxidative stress: In Project Viva we demonstrated protective effects of maternal antioxidants during pregnancy on the risk of wheeze.145,146 In school-aged children the Southern California study 147 has demonstrated modification of ozone effects on incident asthma risk by oxidative stress genotypes. In elderly adults we have demonstrated that genetic polymorphisms related to oxidative stress pathways modulate the cardiac and pulmonary effects of air pollution.148,149 The short-term pulmonary symptom and lung function responses to ozone in asthmatic children have also been demonstrated to be modified by oxidative stress genes in a Mexico City study; antioxidants have reduced adverse ozone effects.150

Home Allergens: In children up through age 7 we have demonstrated associations of early-life home allergens with development of wheeze, asthma, and/or risk of sensitization.81,90,92 Finally, we have measured allergen levels in Project Viva and have the opportunity to evaluate home allergen levels as a modifier of pollution effects on allergy and wheeze. 4. GENERAL PROJECT INFORMATION

EPA Center/Project Viva team: Drs. Gold, Gillman (PI, Project Viva) and Oken, who have been collaborating for more than seven years on Project Viva, bring complementary expertise to this new collaboration with the EPA Center.93,146,151-156 Through the EPA Center as well as NIEHS,157 Dr. Gold has demonstrated pollution effects on vascular dysfunction in adults,44,45 whereas Dr. Gillman’s focus has been on environmental influences on blood pressure in children,102,158-161 as well as in adults, including participants in the Framingham offspring study.159,162,163 With a focus in neurotoxicant effects on cognition, Dr. Oken has worked with Dr. Bellinger,62,106 who has more than 25 years of experience using psychometric testing to evaluate the developmental consequences of various chemical and metabolic insults. Since 2004, he has advised the Project Viva team in the choice of assessment domains and instruments, and trained the research assistants in test administration for both the age 3 and age 7 year visits. Dr. Schwartz contributes specific expertise on pollution effects on child birth weight and cognition. Dr. Gold has collaborated for many years on EPA- and NIEHS-supported air pollution cardio-vascular studies with Drs. Koutrakis, Coull, Schwartz, Zanobetti, Mittleman, and Godleski.

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