Research February 2018 | Volume 126 | Issue 2
The Association of Long-Term Exposure to Particulate Matter Air Pollution with Brain MRI Findings: The ARIC Study
1Milken Institute School of Public Health, George Washington University, Washington, DC, USA
2Gillings School of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA
3RTI International, Research Triangle Park, North Carolina, USA
4School of Medicine, Pennsylvania State University, Hershey, Pennsylvania, USA
5School of Public Health, Texas A&M Health Science Center, College Station, Texas, USA
6Mayo Clinic, Rochester, Minnesota, USA
7Johns Hopkins University, Baltimore, Maryland, USA
8University of Mississippi Medical Center, Jackson, Mississippi, USA
9Carolina Population Center, University of North Carolina, Chapel Hill, North Carolina, USA
10School of Medicine, University of North Carolina, Chapel Hill, North Carolina, USA
PDF Version (165 KB)
- Increasing evidence links higher particulate matter (PM) air pollution exposure to late-life cognitive impairment. However, few studies have considered associations between direct estimates of long-term past exposures and brain MRI findings indicative of neurodegeneration or cerebrovascular disease.
- Our objective was to quantify the association between brain MRI findings and PM exposures approximately 5 to 20 y prior to MRI in the Atherosclerosis Risk in Communities (ARIC) study.
- ARIC is based in four U.S. sites: Washington County, Maryland; Minneapolis suburbs, Minnesota; Forsyth County, North Carolina; and Jackson, Mississippi. A subset of ARIC participants underwent 3T brain MRI in 2011–2013 (n=1,753). We estimated mean exposures to PM with an aerodynamic diameter less than 10 or 2.5 μm (PM10 and PM2.5) in 1990–1998, 1999–2007, and 1990–2007 at the residential addresses of eligible participants with MRI data. We estimated site-specific associations between PM and brain MRI findings and used random-effect, inverse variance–weighted meta-analysis to combine them.
- In pooled analyses, higher mean PM2.5 and PM10 exposure in all time periods were associated with smaller deep-gray brain volumes, but not other MRI markers. Higher PM2.5 exposures were consistently associated with smaller total and regional brain volumes in Minnesota, but not elsewhere.
- Long-term past PM exposure in was not associated with markers of cerebrovascular disease. Higher long-term past PM exposures were associated with smaller deep-gray volumes overall, and higher PM2.5 exposures were associated with smaller brain volumes in the Minnesota site. Further work is needed to understand the sources of heterogeneity across sites. https://doi.org/10.1289/EHP2152
Received: 5 May 2017
Revised: 9 January 2018
Accepted: 10 January 2018
Published: 16 February 2018
Address correspondence to M.C. Power, Department of Epidemiology and Biostatistics, George Washington University, Milken Institute School of Public Health, 950 New Hampshire Avenue NW, 5th Floor, Washington, DC 20052 USA. Telephone: (202) 994-7778. Email: email@example.com
Supplemental Material is available online (https://doi.org/10.1289/EHP2152).
R.F.G. is Associate Editor for Neurology. C.R.J. serves on a scientific advisory board for Eli Lilly & Company and receives research support from the NIH/NIA (R01-AG011378, U01-HL096917, U01-AG024904, RO1 AG041851, R01 AG37551, R01AG043392, U01-AG06786) and the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Foundation. All other authors declare they have no actual or potential competing financial interests.
Note to readers with disabilities: EHP strives to ensure that all journal content is accessible to all readers. However, some figures and Supplemental Material published in EHP articles may not conform to 508 standards due to the complexity of the information being presented. If you need assistance accessing journal content, please contact firstname.lastname@example.org. Our staff will work with you to assess and meet your accessibility needs within 3 working days.
Supplemental Material PDF (271 KB)
Note to readers with disabilities: EHP has provided a 508-conformant table of contents summarizing the Supplemental Material for this article (see below) so readers with disabilities may determine whether they wish to access the full, nonconformant Supplemental Material. If you need assistance accessing journal content, please contact email@example.com. Our staff will work with you to assess and meet your accessibility needs within 3 working days.
Supplemental Table of Contents PDF (26 KB)
Common environmental pollutants may promote cognitive decline, cognitive impairment, and dementia. In particular, recent epidemiologic studies have reported that higher exposure to particulate air pollution is associated with increased risk of cognitive decline, cognitive impairment, and dementia (Power et al. 2016a; Tzivian et al. 2016; Xu et al. 2016). Although this body of work is highly suggestive, work linking air pollution to MRI markers of brain injury may provide mechanistic insight and would allay concerns about residual confounding by sociodemographic and socioeconomic characteristics that are common to studies of air pollution and cognition (Casanova et al. 2016; Chen et al. 2015; Wilker et al. 2015; Wilker et al. 2016). However, relatively little work has been done to examine the link between particulate air pollution and available markers of brain injury, and prior studies exclusively report on associations between recent air pollution exposures and markers of brain injury (Chen et al. 2015; Power et al. 2016a; Wilker et al. 2015). However, current brain health is a result of cumulative causes of brain injury that likely accumulate over decades, including aggregating proteins, ischemic injury, inflammation and oxidative stress, or exposure to toxins. As such, it is reasonable to expect that air pollution exposures over the prior years to decades may significantly contribute to current brain health. In addition, prior studies on air pollution and markers of brain injury are limited by lack of understanding of the selection process by which persons were selected for neuroimaging, which may lead to bias (Weuve et al. 2015).
To address these limitations, we conducted a study to quantify the association of long-term past exposure to particulate matter air pollution with MRI markers of neurodegeneration and subclinical cerebrovascular disease in older adults from the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS). We hypothesized that long-term past exposure to particulate matter (PM) air pollution, specifically PM <2.5 μm in aerodynamic diameter (PM2.5), would be associated with smaller total brain volumes, as atrophy is an etiologically nonspecific indicator of cumulative brain damage, and increased risk of subclinical cerebrovascular disease. We also considered associations with regional brain volumes, given focal atrophy may suggest that PM exposures contribute to the pathogenesis of specific neurodegenerative processes.
In 1987–1989 (Visit 1), the ARIC Study recruited 15,792 participants from four U.S. communities: Minneapolis, Minnesota suburbs; Jackson, Mississippi; Washington County, Maryland; and Forsyth County, North Carolina. Participants have since been invited to complete four additional study visits: Visit 2, 1990–1992; Visit 3, 1993–1995; Visit 4, 1996–1998; and Visit 5, 2011–2013. A sample of participants who attended Visit 5 were invited to undergo brain MRI as part of the ARIC-NCS (Knopman et al. 2015). Briefly, at each site, excluding those with contraindications to MRI, all persons who had any indication of cognitive impairment at Visit 5, all persons who had previously completed brain MRI as part of an ARIC substudy, and a stratified random sample of the remaining participants (stratified by age) were invited to complete a brain MRI. Of those who completed brain MRI (n=1,978), we excluded those with a history of surgery or radiation to the head, multiple sclerosis, or brain tumor (n=15), all nonblack or nonwhite participants from any study site and all black individuals from Minnesota or Maryland (n=15), those with an implausible estimated intracranial volume (eTIV) (n=2), and those for whom we were unable to estimate historical air pollution exposures (n=193). This study was approved by the institutional review boards of all participating institutions. All subjects provided written informed consent to participate at each study visit.
Particulate Matter Air Pollution Exposures
Based on each participant’s residential address, which was updated at each ARIC study visit, we estimated monthly exposures to PM2.5 and PM10 (PM with an aerodynamic diameter <10 μm) using validated spatiotemporal statistical models (Paciorek et al. 2009; Yanosky et al. 2014; Yanosky et al. 2008; Yanosky et al. 2009). These models used PM monitoring, and geographic and meteorological covariates, in conjunction with spatial smoothing, to describe monthly PM2.5 and PM10 levels with high spatial resolution. Given national monitoring data were available for PM2.5 only for 1999 onward, separate spatiotemporal models for PM2.5 were fit for the 1988–1998 and 1999–2007 time periods. The PM2.5 model for the earlier time period (1988–1998) relied on PM10 model predictions and had a simpler space–time structure. The PM2.5 models for both time periods had high predictive accuracy [cross-validation (CV) R2=0.77 for both 1988–1998 and 1999–2007]. The predictive ability of the PM10 model was slightly lower (CV R2=0.58 for both 1988–1998 and 1999–2007). Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region [CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999–2007 (Yanosky et al. 2014)]. As our study sites are located in the Northeast, Midwest, and Southeast, predictive performance is expected to be similar across study sites.
Input data were available from 1988 onward; we generated PM estimates at the residential address of each participant from 1990–2007, given lower confidence in PM estimates in the first few years covered by the model and our goal to quantify associations with long-term past exposures. We did not use moving averages to avoid issues of bias due to secular trends in air pollution coupled with differences in brain health for those who underwent MRI early or late in the study period.
Specifically, among those participants with complete air pollution exposure estimates, we created three exposure summaries for use in our analyses. First, we considered average exposures from 1990–2007, which represents the period approximately 22 to 5 y prior to neuroimaging. We hypothesized that these long-term cumulative exposures would be most relevant to current brain health. Structural brain changes detectable on MRI considered here are expected to represent the culmination of years of brain injury; thus, long-term cumulative average exposure would be expected to be relevant to the severity of brain injury detectable on MRI. In addition, we also separately considered average exposures from 1990–1998 (approximately 14 to 22 y prior to neuroimaging) and from 1999–2007 (approximately 14 to 5 y prior to neuroimaging), to explore whether changes to exposure model before and after 1999 impacted our findings. However, we recognize that, if observed, differences in association across averaging periods could also be attributable to true differences in the impact of exposure based on the timing of exposure relative to outcome assessment.
At each study site, participants completed 3T MRI scans according to a standardized protocol. Pulse sequences included a sagittal T1-weighted 3-D volumetric magnetization-prepared gradient echo (MPRAGE) pulse sequence, axial T2 fluid-attenuated inversion recovery and axial T2* weighted gradient echo. The ARIC MRI reading center (Mayo Clinic, Minnesota) analyzed all images.
Regional gray-matter volumes were quantified with FreeSurfer (version 5.1; Laboratory for Computational Neuroimaging at the Athinoula A. Martinos Center for Biomedical Imaging), and total brain and intracranial volumes were estimated using in-house algorithms (Jack et al. 2014). In our analyses, we consider gray-matter volumes of the total brain, the four lobes (frontal, parietal, temporal, occipital), the hippocampus, the deep-gray structures (thalamus, caudate, putamen, and pallidum), and total volume of multiple gray-matter regions known to atrophy preferentially in Alzheimer’s disease (parahippocampal, entorhinal, and inferior parietal lobules, hippocampus, precuneus, and cuneus), which we refer to as the AD signature region (Dickerson et al. 2011).
White matter hyperintensity (WMH) volumes were measured using an in-house algorithm. (Raz et al. 2013) As WMH volumes were not normally distributed, we created a dichotomous severe WMH variable defined as present if WMH volume is >5% of total white matter volume. Brain infarcts and microbleeds were identified, counted, and measured by a trained imaging technician and confirmed by a radiologist (Knopman et al. 2015). Lacunar infarcts were subsequently identified based on location and size (3–15 mm in diameter) (Wardlaw et al. 2013). Microbleeds were subsequently classified as lobar or subcortical based on location. In our analyses, we characterized infarcts, lacunar infarcts, microbleeds, lobar microbleeds, and subcortical microbleeds as present or absent.
We used data collected at Visits 1 and 4 to define participant age, gender (male/female), education (≤high school, >high school), body mass index (BMI; normal/overweight/obese), and smoking status (current/former/never). BMI was defined as measured weight (kg) divided by the square of measured height (m), while all other covariates were defined via self-report. We also considered two measures of area-level socioeconomic status (SES), the proportion of the residential census tract population below the U.S. poverty line, and a summary measure of neighborhood wealth/income, education, and occupation combining U.S. Census tract–level characteristics denoted the Neighborhood SES score (Diez Roux et al. 2001). Each measure of SES was categorized into three levels (bottom quintile, middle three quintiles, top quintile) using center-specific cutoffs.
We initially conducted all analyses stratified by study site. Brain volumes were z-transformed prior to use in analyses based on the mean and standard deviation (SD) of volumes in those individuals who met eligibility criteria for inclusion in our study. We used weighted linear or logistic regression to quantify the site-specific association between a 1–μg/m3-higher PM exposure measure and each of our neuroimaging features. The weights accounted for the stratified random sampling used to select participants from each site from ARIC Visit 5 into the ARIC MRI sample; thus, our site-specific analyses can be interpreted as the association that would be observed in the full Visit 5 ARIC sample at each site. All models were adjusted for age, gender, race, education, and eTIV. Associations with 1990–1998 and 1990–2007 exposure summaries were adjusted for covariate values at the time of Visit 1 (1987–1989), while associations with 1999–2007 exposure summaries were adjusted for covariate values at the time of Visit 4 (1996–1998). To provide a summary estimate combining data from all four sites, we combined site-specific estimates using random effects meta-analysis (DerSimonian and Laird 1986). Use of random effects meta-analysis was chosen given potential heterogeneity in association due to differences in PM composition or other factors across study sites. It also allowed for formal evaluation of the evidence for heterogeneity across estimates using the I2 test. Moreover, this method has the benefit of allowing us to derive a summary measure of association despite evidence of intractable confounding by site; exposure and confounder distributions across sites did not always overlap.
In sensitivity analyses, we reestimated our site-specific and combined estimates of association a) additionally adjusting for BMI, smoking status, and our two measures of area-level SES; b) excluding persons with documented stroke before MRI; c) restricting to persons who did not move during follow-up; d) considering white participants only (there were too few black participants in the North Carolina site to allow a site-specific estimate among blacks or a pooled estimate combining the North Carolina and Mississippi site estimates); e) incorporating inverse probability of attrition weighting (Hernán et al. 2000; Power et al. 2016b) to account for potentially informative attrition from Visit 1 to Visit 5; f) excluding potential outliers in our exposure estimates through application of the generalized extreme studentized derivative test (Rosner 1983); g) using log-transformed WMH volumes as an outcome in linear regression models; and h) in models omitting weighting. We also reestimated our site-specific estimates of association using a 1-SD unit increase in site-specific exposure as the exposure contrast. All analyses were completed using SAS (version 9.4; SAS Institute Inc.) or Stata (version 14.0; StataCorp).
In total, 1,753 persons met our eligibility criteria and were included in the analyses. At the time of MRI, participants were on average 76 y old, 40% were male, and 45% had greater than a high school education. Table 1 provides demographic and clinical characteristics of the study participants, as well as information on our MRI outcomes by study site. Overall, the Minnesota site was the most affluent of the four sites, followed in order by North Carolina, Maryland, and Mississippi.
|Characteristic||MN (n=419)% or mean±SD||MD (n=443)% or mean±SD||NC (n=446)% or mean±SD||MS (n=441)% or mean±SD||p-Valuea|
|Age at baseline, y||53±5||53±5||54±5||52±5||0.0004|
|Age at MRI, y||76±5||77±5||77±5||75±5|
|Smoking at baseline||<0.0001|
|BMI at baseline, kg/m2||27±4||27±5||25±4||29±5||<0.0001|
|Neighborhood SES score at baseline||4.4±3.1||−0.4±2.8||−3.4±5.1||−4.6±4.6||<0.0001|
|Proportion of residential census track below U.S. poverty line at baseline||0.05±0.03||0.08±0.05||0.07±0.06||0.31±0.14||<0.0001|
|Estimated intracranial volume, cm3||1436±154||1378±150||1406±159||1308±134||<0.0001|
|Total brain volume, cm3||1048±109||1009±104||1022±108||967±98||0.05|
|Frontal lobe volume, cm3||155±15||149±15||153±17||143±14||<0.0001|
|Parietal lobe volume, cm3||111±12||106±12||107±12||99±11||<0.0001|
|Occipital lobe volume, cm3||43±5||41±5||41±5||37±5||<0.0001|
|Temporal lobe volume, cm3||105±12||101±11||102±12||98±11||<0.0001|
|Deep-gray volume, cm3||30±3||30±3||30±3||29±3||0.004|
|Hippocampal volume, cm3||7.0±0.9||6.7±0.9||6.9±1.0||6.8±1.0||0.002|
|AD signature region volume, cm3||62±7||59±7||59±7||56±6||<0.0001|
|Subcortical microbleeds present||20||17||22||23||0.0005|
|Lobar microbleeds present||10||7||10||9||0.0003|
Note: ARIC-NCS, Atherosclerosis Risk in Communities Neurocognitive Study; BMI, body mass index; HS, high school; MD, Maryland; MN, Minnesota; MS, Mississippi; NC, North Carolina; SD, standard deviation; SES, socioeconomic status; WMH, white matter hyperintensities.
aChi-square of F-test p-value for comparison of characteristics by site, after weighting; p-values for brain volumes are additionally adjusted for estimated intracranial volume.
bSevere WMH defined as WMH volume >5% of white matter volume.
Of the four sites, Minnesota and Mississippi had the lowest PM exposures, while Maryland and North Carolina had the highest (Table 2). Variation in exposure to PM10 was generally larger than variation in exposure to PM2.5. Site-specific coefficients of variation for our exposure estimates ranged from 0.03 to 0.11 μg/m3 for PM10 and 0.02 to 0.10 μg/m3 for PM2.5.
|Exposure||Site||Time period||Mean||SD||Minimum||25th Percentile||75th Percentile||Maximum|
Note: ARIC-NCS, Atherosclerosis Risk in Communities Neurocognitive Study; MD, Maryland; MN, Minnesota; MS, Mississippi; NC, North Carolina; PM, particulate matter; SD, standard deviation.
As there was evidence of moderate to high heterogeneity (I2>40%) across sites when considering analyses of PM2.5 and brain volumes, we discuss both the site-specific and pooled analyses (Table 3). In the Minnesota site, higher PM2.5 exposures were generally associated with smaller total and regional brain volumes, with slightly stronger associations observed when considering the 1990–1998 exposure period compared to the 1999–2007 exposure period. This pattern was not observed in the other three sites. Results from the Maryland and North Carolina sites were consistently null. In the Mississippi site, there was some evidence to support a protective association between higher PM exposures and larger AD signature region, temporal lobe, and occipital lobe volumes, regardless of exposure period; associations with other regions were typically null. When site-specific associations were pooled via meta-analysis, the resulting effect estimates were generally null. However, consistently adverse associations between PM2.5 exposure from 1999–2007 and frontal lobe volumes across sites resulted in a small, marginally significant pooled association [beta: −0.02 SD units per 1–μg/m3 higher exposure; 95% confidence interval (CI): −0.04, 0.00] Similarly, consistently adverse associations between higher PM2.5 exposures in all three time periods and smaller deep-gray volumes across the Minnesota, Maryland, and North Carolina sites resulted in small, marginally significant pooled associations (e.g., for mean PM2.5 from 1990–2007, beta: −0.03 SD units per 1–μg/m3 higher exposure; 95% CI: −0.08, 0.00). The overall pattern of site-specific and combined results was similar across our sensitivity analyses, including analyses implementing inverse probability weighting (Tables S1 and S2) and those omitting use of sampling weights (Table S3).
|Outcome and site||n||PM2.5 1990–1998||PM2.5 1999–2007||PM2.5 1990–2007|
|Beta (95% CI)||p-Value||I2/p-Value for heterogeneity||Beta (95% CI)||p-Value||I2/p-Value for heterogeneity||Beta (95% CI)||p-Value||I2/p-Value for heterogeneity|
|MN||419||−0.09 (−0.16, −0.01)||0.02||—||−0.07 (−0.11, −0.03)||<0.01||—||−0.1 (−0.16, −0.05)||<0.01||—|
|MD||443||0.01 (−0.03, 0.05)||0.58||—||0 (−0.02, 0.02)||0.84||—||0 (−0.03, 0.03)||0.96||—|
|NC||446||0.03 (−0.05, 0.11)||0.47||—||−0.02 (−0.09, 0.05)||0.54||—||0 (−0.09, 0.08)||0.93||—|
|MS||441||0.07 (−0.09, 0.23)||0.38||—||0.04 (−0.08, 0.16)||0.52||—||0.07 (−0.09, 0.22)||0.39||—|
|Combined||—||0 (−0.06, 0.05)||0.87||58.1/0.07||−0.02 (−0.07, 0.02)||0.31||72.9/0.01||−0.02 (−0.09, 0.04)||0.5||75.8/0.01|
|MN||417||−0.1 (−0.2, 0)||0.04||—||−0.05 (−0.11, 0.01)||0.08||—||−0.09 (−0.17, 0)||0.04||—|
|MD||442||−0.01 (−0.06, 0.04)||0.76||—||−0.01 (−0.04, 0.01)||0.27||—||−0.02 (−0.05, 0.02)||0.36||—|
|NC||446||0.02 (−0.09, 0.13)||0.69||—||−0.01 (−0.09, 0.06)||0.75||—||0 (−0.11, 0.11)||1||—|
|MS||440||0.07 (−0.08, 0.23)||0.36||—||−0.02 (−0.15, 0.11)||0.72||—||0.02 (−0.14, 0.18)||0.81||—|
|Combined||—||−0.01 (−0.07, 0.04)||0.63||35.8/0.20||−0.02 (−0.04, 0)||0.08||0/0.66||−0.02 (−0.05, 0.01)||0.13||0/0.41|
|MN||417||−0.09 (−0.2, 0.03)||0.13||—||−0.06 (−0.13, 0)||0.06||—||−0.1 (−0.19, 0)||0.05||—|
|MD||442||0 (−0.06, 0.06)||0.97||—||0 (−0.03, 0.02)||0.82||—||0 (−0.04, 0.04)||0.91||—|
|NC||446||0.01 (−0.13, 0.14)||0.92||—||−0.07 (−0.16, 0.03)||0.17||—||−0.06 (−0.2, 0.08)||0.43||—|
|MS||440||0.23 (−0.02, 0.47)||0.07||—||0.15 (−0.01, 0.31)||0.06||—||0.23 (0.02, 0.45)||0.04||—|
|Combined||—||0 (−0.08, 0.08)||1||46.5/0.13||−0.02 (−0.08, 0.04)||0.59||63.5/0.04||−0.01 (−0.1−0.1, 0.08)||0.79||64.0/0.04|
|MN||417||−0.1 (−0.2, −0.01)||0.04||—||−0.07 (−0.14, 0.01)||0.08||—||−0.1 (−0.2, 0)||0.05||—|
|MD||442||0.01 (−0.04, 0.06)||0.65||—||0 (−0.03, 0.02)||0.83||—||0 (−0.04, 0.04)||0.99||—|
|NC||446||0.01 (−0.08, 0.1)||0.82||—||−0.03 (−0.09, 0.03)||0.34||—||−0.02 (−0.11, 0.07)||0.63||—|
|MS||440||0.09 (−0.1, 0.29)||0.36||—||0.05 (−0.1, 0.19)||0.54||—||0.08 (−0.11, 0.27)||0.39||—|
|Combined||—||−0.01 (−0.07, 0.05)||0.77||44.4/0.15||−0.01 (−0.04, 0.01)||0.33||15.3/0.15||−0.02 (−0.07, 0.03)||0.48||34.4/0.21|
|MN||417||−0.08 (−0.17, 0.01)||0.08||—||−0.05 (−0.1, 0.01)||0.09||—||−0.07 (−0.15, 0)||0.05||—|
|MD||442||−0.01 (−0.07, 0.04)||0.59||—||0 (−0.03, 0.02)||0.82||—||−0.01 (−0.04, 0.03)||0.75||—|
|NC||446||0.03 (−0.07, 0.13)||0.54||—||0 (−0.08, 0.08)||0.91||—||0.02 (−0.09, 0.13)||0.71||—|
|MS||440||0.24 (0.03, 0.45)||0.02||—||0.11 (−0.04, 0.26)||0.14||—||0.21 (0.01, 0.41)||0.04||—|
|Combined||—||0.01 (−0.07, 0.09)||0.85||65.5/0.03||−0.01 (−0.04, 0.03)||0.68||35.7/0.20||0 (−0.07, 0.07)||0.99||62.5/0.05|
|MN||417||−0.08 (−0.21, 0.04)||0.19||—||−0.06 (−0.13, 0.01)||0.07||—||−0.09 (−0.2, 0.01)||0.07||—|
|MD||442||−0.04 (−0.1, 0.02)||0.15||—||−0.01 (−0.04, 0.01)||0.29||—||−0.03 (−0.06, 0.01)||0.18||—|
|NC||446||−0.04 (−0.18, 0.1)||0.55||—||−0.04 (−0.13, 0.05)||0.35||—||−0.06 (−0.19, 0.07)||0.38||—|
|MS||440||0.11 (−0.1, 0.32)||0.3||—||0.04 (−0.12, 0.21)||0.61||—||0.09 (−0.12, 0.29)||0.39||—|
|Combined||—||−0.04 (−0.09, 0.01)||0.1||0/0.48||−0.02 (−0.04, 0)||0.09||0/0.46||−0.03 (−0.07, 0)||0.07||1.5/0.38|
|MN||416||−0.01 (−0.14, 0.12)||0.87||—||−0.05 (−0.12, 0.02)||0.15||—||−0.06 (−0.17, 0.05)||0.28||—|
|MD||442||−0.02 (−0.1, 0.05)||0.53||—||0 (−0.03, 0.04)||0.83||—||0 (−0.05, 0.05)||1||—|
|NC||443||0.07 (−0.05, 0.2)||0.27||—||−0.04 (−0.17, 0.1)||0.6||—||0 (−0.15, 0.16)||0.99||—|
|MS||437||0.07 (−0.32, 0.45)||0.74||—||0.12 (−0.16, 0.41)||0.39||—||0.13 (−0.25, 0.51)||0.5||—|
|Combined||—||0 (−0.06, 0.06)||0.99||0/0.63||−0.01 (−0.04, 0.02)||0.64||0/0.40||−0.01 (−0.05, 0.04)||0.71||0/0.68|
|MN||417||−0.09 (−0.19, 0.02)||0.11||—||−0.03 (−0.1, 0.04)||0.39||—||−0.06 (−0.15, 0.03)||0.2||—|
|MD||442||0.01 (−0.05, 0.06)||0.8||—||−0.01 (−0.03, 0.02)||0.7||—||0 (−0.04, 0.03)||0.86||—|
|NC||446||0.01 (−0.09, 0.11)||0.85||—||−0.02 (−0.09, 0.05)||0.57||—||−0.01 (−0.12, 0.09)||0.8||—|
|MS||440||0.2 (−0.02, 0.42)||0.07||—||0.12 (−0.03, 0.27)||0.12||—||0.2 (−0.01, 0.4)||0.06||—|
|Combined||—||0 (−0.07, 0.07)||0.97||48.8/0.12||−0.01 (−0.03, 0.02)||0.63||9.2/0.35||0 (−0.06, 0.05)||0.86||41.0/0.17|
Note: Adjusted for age, gender, race, education and estimated intracranial volume. —, no data; ARIC-NCS, Atherosclerosis Risk in Communities Neurocognitive Study; CI, confidence interval; MD, Maryland; MN, Minnesota; MS, Mississippi; NC, North Carolina; PM, particulate matter.
Similarly, there was some evidence to suggest heterogeneity of association across sites when considering analyses of PM10 and brain volumes (Table 4). Site-specific analyses suggested adverse associations between higher mean PM10 over 1999–2007 or 1990–2007 and smaller total brain volumes, occipital lobe volumes, and deep-gray volumes in Minnesota (Table 4). As with the PM2.5 analyses, we also observed protective associations between higher long-term PM10 exposure and larger occipital lobe, temporal lobe, and AD signature region volumes in Mississippi. As with PM2.5, there was little evidence of an association between long-term PM10 exposure and total or regional brain volumes in pooled analyses, with the exception of an adverse association between higher mean PM10 in all three time periods and smaller deep-gray-region volumes (e.g., the PM10 1990–2007 time period, beta: −0.02; 95% CI: −0.04, 0.00). As above, the overall pattern of site-specific and combined results was similar across sensitivity analyses, including analyses implementing inverse probability weighting (Tables S4 and S5) and those omitting use of sampling weights (Table S6).
|Outcome and site||n||PM2.5 1990–1998||PM2.5 1999–2007||PM2.5 1990–2007|
|Beta (95% CI)||p-Value||I2/p-Value for heterogeneity||Beta (95% CI)||p-Value||I2/p-Value for heterogeneity||Beta (95% CI)||p-Value||I2/p-Value for heterogeneity|
|MN||419||−0.01 (−0.03, 0.01)||0.56||—||−0.02 (−0.05, 0)||0.06||—||−0.02 (−0.04, 0.01)||0.16||—|
|MD||443||0 (−0.01, 0.02)||0.75||—||0.01 (−0.01, 0.02)||0.51||—||0 (−0.01, 0.02)||0.64||—|
|NC||446||0.01 (−0.05, 0.07)||0.85||—||0.01 (−0.06, 0.07)||0.85||—||0.01 (−0.06, 0.07)||0.83||—|
|MS||441||0.03 (−0.05, 0.11)||0.42||—||0.03 (−0.03, 0.09)||0.27||—||0.04 (−0.04, 0.11)||0.31||—|
|Combined||—||0 (−0.01, 0.01)||0.96||0/0.77||0 (−0.02, 0.02)||0.9||41.8/0.16||0 (−0.02, 0.01)||0.86||7.9/0.35|
|MN||417||0.01 (−0.02, 0.04)||0.49||—||−0.01 (−0.04, 0.02)||0.52||—||0 (−0.03, 0.03)||0.87||—|
|MD||442||0 (−0.03, 0.02)||0.69||—||0 (−0.03, 0.02)||0.84||—||0 (−0.03, 0.02)||0.75||—|
|NC||446||0 (−0.08, 0.08)||0.99||—||0.03 (−0.05, 0.11)||0.51||—||0.01 (−0.07, 0.1)||0.73||—|
|MS||440||0.02 (−0.05, 0.1)||0.54||—||0 (−0.06, 0.06)||1||—||0.01 (−0.06, 0.09)||0.74||—|
|Combined||—||0 (−0.01, 0.02)||0.81||0/0.82||0 (−0.02, 0.01)||0.7||0/0.86||0 (−0.02, 0.02)||0.86||0/0.96|
|MN||417||−0.02 (−0.05, 0.01)||0.24||—||−0.03 (−0.05, 0)||0.05||—||−0.03 (−0.06, 0)||0.09||—|
|MD||442||0 (−0.03, 0.03)||0.87||—||0.01 (−0.02, 0.04)||0.64||—||0 (−0.03, 0.03)||0.9||—|
|NC||446||0.01 (−0.09, 0.12)||0.82||—||0.03 (−0.08, 0.13)||0.62||—||0.02 (−0.09, 0.13)||0.72||—|
|MS||440||0.1 (−0.02, 0.22)||0.1||—||0.1 (0.01, 0.19)||0.02||—||0.11 (0, 0.22)||0.05||—|
|Combined||—||0 (−0.03, 0.02)||0.77||21.6/0.28||0.01 (−0.03, 0.05)||0.6||66.8/0.03||0 (−0.03, 0.04)||0.86||54.3/0.09|
|MN||417||0.01 (−0.02, 0.04)||0.42||—||−0.02 (−0.05, 0.01)||0.29||—||−0.01 (−0.03, 0.02)||0.65||—|
|MD||442||0 (−0.02, 0.03)||0.72||—||0 (−0.02, 0.03)||0.84||—||0 (−0.02, 0.03)||0.78||—|
|NC||446||0.02 (−0.05, 0.09)||0.6||—||0.04 (−0.02, 0.1)||0.22||—||0.03 (−0.04, 0.1)||0.38||—|
|MS||440||0.04 (−0.05, 0.14)||0.38||—||0.05 (−0.02, 0.13)||0.18||—||0.06 (−0.04, 0.15)||0.24||—|
|Combined||—||0.01 (−0.01, 0.02)||0.31||0/0.85||0 (−0.02, 0.03)||0.71||31.8/0.22||0 (−0.01, 0.02)||0.75||0/0.51|
|MN||417||0.01 (−0.02, 0.03)||0.65||—||−0.01 (−0.04, 0.02)||0.57||—||0 (−0.04, 0.03)||0.84||—|
|MD||442||−0.01 (−0.03, 0.01)||0.47||—||−0.01 (−0.03, 0.02)||0.58||—||−0.01 (−0.03, 0.02)||0.5||—|
|NC||446||0.02 (−0.05, 0.09)||0.66||—||0.04 (−0.02, 0.11)||0.2||—||0.03 (−0.04, 0.1)||0.39||—|
|MS||440||0.12 (0.02, 0.23)||0.02||—||0.1 (0.02, 0.17)||0.01||—||0.12 (0.03, 0.22)||0.01||—|
|Combined||—||0.01 (−0.02, 0.04)||0.52||51.1/0.11||0.02 (−0.02, 0.05)||0.37||65.2/0.03||0.01 (−0.02, 0.05)||0.43||61.0/0.05|
|MN||417||−0.01 (−0.04, 0.01)||0.27||—||−0.03 (−0.05, 0)||0.04||—||−0.02 (−0.05, 0)||0.04||—|
|MD||442||−0.02 (−0.05, 0.01)||0.12||—||−0.02 (−0.05, 0.01)||0.15||—||−0.02 (−0.05, 0.01)||0.12||—|
|NC||446||−0.04 (−0.14, 0.06)||0.44||—||−0.01 (−0.11, 0.09)||0.78||—||−0.03 (−0.14, 0.08)||0.59||—|
|MS||440||0.05 (−0.05, 0.15)||0.33||—||0.04 (−0.04, 0.12)||0.3||—||0.05 (−0.04, 0.14)||0.28||—|
|Combined||—||−0.02 (−0.03, 0)||0.07||0/0.57||−0.02 (−0.04, 0)||0.03||0/0.45||−0.02 (−0.04, 0)||0.02||0/0.49|
|MN||416||−0.01 (−0.04, 0.03)||0.69||—||−0.02 (−0.04, 0.01)||0.13||—||−0.02 (−0.04, 0.01)||0.24||—|
|MD||442||−0.02 (−0.05, 0.02)||0.35||—||−0.01 (−0.04, 0.03)||0.75||—||−0.01 (−0.05, 0.02)||0.5||—|
|NC||443||0.06 (−0.03, 0.16)||0.2||—||0.08 (−0.02, 0.19)||0.1||—||0.08 (−0.03, 0.19)||0.14||—|
|MS||437||0.05 (−0.14, 0.24)||0.62||—||0.08 (−0.05, 0.22)||0.23||—||0.08 (−0.1, 0.25)||0.38||—|
|Combined||−0.01 (−0.03, 0.02)||0.58||0/0.46||0 (−0.03, 0.04)||0.87||47.9/0.12||−0.01 (−0.03, 0.02)||0.7||25.1/0.26|
|MN||417||0 (−0.02, 0.03)||0.88||—||−0.01 (−0.04, 0.01)||0.28||—||−0.01 (−0.03, 0.02)||0.49||—|
|MD||442||0 (−0.02, 0.02)||0.99||—||0 (−0.03, 0.02)||0.93||—||0 (−0.03, 0.02)||0.96||—|
|NC||446||0.02 (−0.06, 0.1)||0.59||—||0.05 (−0.03, 0.14)||0.2||—||0.04 (−0.05, 0.13)||0.36||—|
|MS||440||0.1 (−0.01, 0.21)||0.07||—||0.12 (0.03, 0.2)||0.01||—||0.12 (0.02, 0.23)||0.02||—|
|Combined||—||0 (−0.01, 0.02)||0.6||12.0/0.33||0.02 (−0.02, 0.06)||0.35||70.1/0.02||0.01 (−0.02, 0.04)||0.54||53.5/0.09|
Note: Adjusted for age, gender, race, education, and estimated intracranial volume. —, no data; ARIC-NCS, Atherosclerosis Risk in Communities Neurocognitive Study; MD, Maryland; MN, Minnesota; MS, Mississippi; NC, North Carolina; PM, particulate matter.
When considering the relation between PM2.5 or PM10 and the presence of MRI markers of cerebrovascular disease, there was little statistical evidence of heterogeneity of association across the four sites; thus, we focused on the analyses pooling estimates from all four sites via meta-analysis. Overall, there was little conclusive evidence to support an association between higher exposure to PM2.5 or PM10 in any time period and the presence of MRI markers of cerebrovascular disease in pooled analyses combining all four sites (Tables S7 and S8). However, the odds ratios (ORs) for the pooled associations between a 1–μg/m3-higher mean PM2.5 exposure and either lacunes or subcortical microbleeds were consistently in the range of 1.04 to 1.10, although these associations were not statistically significant. Similarly, although the ORs for the pooled associations between a 1–μg/m3-higher mean PM10 exposure and microbleeds were consistently in the range of 1.04 to 1.05; these associations were also not statistically significant. Results from our sensitivity analyses were broadly consistent with our primary analysis findings, including analyses implementing inverse probability weighting or omitting use of sampling weights (Tables S9 to S14).
In pooled analyses combining all four sites, higher mean PM2.5 and PM10 exposures in the 5 to 20 y prior were associated with smaller deep-gray regional brain volumes and higher PM2.5 exposures 5–14 y prior were marginally associated with smaller frontal lobe volumes. We found little evidence in support of an association between higher long-term exposure to PM2.5 or PM10 over our three time periods of exposure and other brain volume measures or markers of cerebrovascular and small vessel disease in pooled analyses. However, there was evidence of significant heterogeneity in associations between PM and brain volumes by study site. When considering site-specific associations, we consistently observed smaller total and regional brain volumes with greater long-term exposure to PM2.5 in the Minnesota site, but not the other three sites. Throughout, where there was evidence of an association between PM exposure and brain volumes, the magnitude of these associations was similar to that seen in prior analyses in this sample, considering the association between midlife blood pressure and brain volumes. (Power et al. 2016b) For reference, the −0.05 to −0.1 SD unit effect size observed in the Minnesota site can be interpreted as loss of approximately 0.5% to 1% of regional brain volume.
Strengths of this study include the relatively large number of participants with MRI, our ability to use weighting to account for selection into the MRI subcohort in primary analyses and attrition from the baseline ARIC visit in sensitivity analyses, and consideration of long-term, cumulative past exposures. Our coefficients of variation for air pollution exposure estimates are similar to those calculated from other studies based in geographically constrained locations (albeit typically using shorter averaging periods) (Power et al. 2016a), suggesting that the variation in exposure at our four sites is similar to that found in other locations. However, the relatively small number of persons in each center limits our power to detect small true effects, systematically evaluate the potential for nonlinear associations, or assess effect modification by age or other personal factors. In addition, mild brain atrophy may have several root causes. Heterogeneity in the causes of neurodegeneration in our sample may contribute to the muted dose–response, especially if only a subset of the potential causes of neurodegeneration, including both neurodegenerative diseases and other sources of brain injury, are related to air pollution exposure. Similarly, our findings do not preclude the possibility of neurotoxic effects on the brain that are not captured by the considered MRI markers of brain injury; studies considering alternate markers (e.g., cortical thickness) may be useful. We did not consider associations with more recent exposures, or with cumulative exposures that include recent exposures. As such, we cannot comment on the relative importance of recent versus past exposures or whether recent exposures are an acceptable surrogate for long-term cumulative exposures. As with many recent studies of the health effects of air pollution, we used modeled exposure measures using residential address rather than personal exposure metrics, and we were unable to address the issue of indoor air pollution. Moreover, we cannot discount the possibility that regional variation in predictive accuracy of our model may complicate or invalidate comparison of site-specific effect estimates. Finally, we cannot exclude the possibility of chance findings.
There are a small number of reports considering the association between PM exposures and MRI-based measurements of brain structure or subclinical cerebrovascular disease. Collectively, including the current study, this body of literature fails to identify a consistent pattern of associations, as results are frequently null, with the few positive findings differing across studies. In a study nested within the Women’s Health Initiative Memory Study (WHIMS), PM2.5 air pollution exposures in 1999–2006 were not associated with gray-matter brain volumes assessed in 2005–2006 (Chen et al. 2015). However, higher PM2.5 exposures were associated with smaller, normal-appearing white matter brain volumes, with magnitudes of association of roughly 0.01-SD units volume per interquartile range increase in exposure. Additional analyses in WHIMS using a voxel-based approach found PM2.5 exposures in the 3 y prior to MRI were associated with areas of smaller cortical gray-matter and subcortical white-matter volumes (Casanova et al. 2016). Notably, the authors also report significant clusters of association whereby higher PM2.5 was associated with larger deep gray–matter nuclei volumes in WHIMS participants (Casanova et al. 2016), opposite to our own observations of associations between higher PM and smaller regional gray-matter volumes in ARIC participants. In participants from the Framingham Offspring Study who lived in the New England region, higher past-year PM2.5 exposure was associated with smaller total cerebral brain volumes and greater risk of covert brain infarcts, but not with WMH volumes, age-adjusted extensive WMH volumes, or hippocampal volumes (Wilker et al. 2015). Finally, in a study of participants from the Massachusetts Alzheimer’s Disease Research Center Longitudinal Cohort, there was no association between higher PM2.5 exposures in 2003 and either brain parenchymal fraction (a measure of brain atrophy) or the presence of microbleeds at an MRI between 2004 and 2010, while there was a protective association between higher PM2.5 exposure and smaller WMH volumes (Wilker et al. 2016).
Interestingly, studies of the relationship between air pollution and cognitive or related outcomes (e.g., MRI markers or neuropathology) that consider geographically localized samples are more likely to report null associations. In contrast, studies considering participants spread over larger geographic regions have been more likely to report adverse associations (Power et al. 2016a). We suggest several potential explanations. First, studies in geographically constrained locations are typically small, and the range of exposures tends to be smaller. Thus, such studies are likely underpowered to detect small effects. A meta-analytic approach, such as demonstrated here, for combining information about multiple small, geographically constrained studies in different locations can overcome this limitation without inducing concerns about strong or intractable confounding that may arise in pooled analyses. Second, studies with wider geographic distribution may be more susceptible to confounding by characteristics that vary regionally. As we have previously demonstrated elsewhere (Power et al. 2016a), it appears unlikely that residual confounding may fully account for the adverse findings in more geographically dispersed settings, given the characteristics such a confounder would have to have in order to fully account for previously observed associations. However, this possibility cannot be fully discounted, especially given evidence in this study that exposure and confounder distributions across sites do not always overlap. Thus, residual confounding may still lead to a biased estimate of the true association in more expansive settings when spatial confounding is strong and meta-analysis of site-specific associations is not used. Finally, it is possible that a focus on quantifying exposure based on particulate mass is contributing to this heterogeneity of findings. If specific PM species or other physical characteristics such as surface area confer the relevant toxic effect, geographically constrained studies may be studying the impact of less toxic exposures, while geographically broad studies may be capturing mixtures of these effects due to their larger study area. Our finding of heterogeneity in association across sites would support this hypothesis, and the finding of adverse associations in Minnesota but not the other three sites may be attributable not to chance, but to the relative toxicity of exposures. Future work will be needed to understand the drivers of the divide in findings between these two study types in order to establish a causal effect of air pollution on late-life brain health.
Another potential explanation for the finding of adverse associations between PM2.5 and brain volumes in the Minnesota site, but not the others, lies in the potential nonlinearity of the association. Minnesota had the lowest air pollution levels of the four sites, and previous studies have suggested a nonlinear relationship between PM and both total cerebral brain volume (Wilker et al. 2015) and cognitive function (Ailshire and Crimmins 2014; Oudin et al. 2015; Power et al. 2011), whereby the strongest associations were observed at the lowest levels of exposure. Given relatively small samples per site, we were not able to assess nonlinearity of exposure within site, but hope others may be able to follow up on this possibility in the future.
In conclusion, we found no associations between cumulative past PM exposure and MRI-based markers of cerebrovascular disease. Combining data across sites, higher past PM exposures were associated with smaller deep-gray volumes across sites, and higher PM2.5 in 1999–2007 was marginally associated with smaller frontal lobe volumes. When considering individual sites, higher PM2.5 exposures were associated with smaller brain volumes in the Minnesota site. Further work will be needed to replicate these findings and understand the sources of heterogeneity across sites, and will require consideration of a broader number of sites.
The ARIC study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts (HHSN268201100005C, HHSN268201100006C, HHSN268201100007C, HHSN268201100008C, HHSN268201100009C, HHSN268201100010C, HHSN268201100011C, and HHSN268201100012C). Neurocognitive data is collected by U01 HL096812, HL096814, HL096899, HL096902, and HL096917, with previous brain MRI examinations funded by R01-HL70825. The sponsors had no role in the design and conduct of the study; collection management, analysis, and interpretation of the data; preparation review; or approval of the manuscript. The authors thank the staff and participants of the ARIC study for their important contributions.
Casanova R, Wang X, Reyes J, Akita Y, Serre ML, Vizuete W. 2016. A voxel-based morphometry study reveals local brain structural alterations associated with ambient fine particles in older women. Front Hum Neurosci 10:495, PMID: 27790103, 10.3389/fnhum.2016.00495.
Chen JC, Wang X, Wellenius GA, Serre ML, Driscoll I, Casanova R, et al. 2015. Ambient air pollution and neurotoxicity on brain structure: evidence from women’s health initiative memory study. Ann Neurol 78(3):466–476, PMID: 26075655, 10.1002/ana.24460.
Dickerson BC, Stoub TR, Shah RC, Sperling RA, Killiany RJ, Albert MS, et al. 2011. Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology 76(16):1395–1402, PMID: 21490323, 10.1212/WNL.0b013e3182166e96.
Diez Roux AV, Merkin SS, Arnett D, Chambless L, Massing M, Nieto FJ, et al. 2001. Neighborhood of residence and incidence of coronary heart disease. N Engl J Med 345(2):99–106, PMID: 11450679, 10.1056/NEJM200107123450205.
Hernán MA, Brumback B, Robins JM. 2000. Marginal structural models to estimate the causal effect of zidovudine on the survival of HIV-positive men. Epidemiology 11(5):561–570, PMID: 10955409, 10.1198/016214501753168154.
Knopman DS, Griswold ME, Lirette ST, Gottesman RF, Kantarci K, Sharrett AR, et al. 2015. Vascular imaging abnormalities and cognition: mediation by cortical volume in nondemented individuals: atherosclerosis risk in communities-neurocognitive study. Stroke 46(2):433–440, PMID: 25563642, 10.1161/STROKEAHA.114.007847.
Oudin A, Forsberg B, Adolfsson AN, Lind N, Modig L, Nordin M, et al. 2015. Traffic-related air pollution and dementia incidence in northern Sweden: a longitudinal study. Environ Health Perspect 124(3):306–312, PMID: 26305859, 10.1289/ehp.1408322.
Paciorek CJ, Yanosky JD, Puett RC, Laden F, Suh HH. 2009. Practical large-scale spatio-temporal modeling of particulate matter concentrations. Ann Appl Stat 3(1):370–397, 10.1214/08-AOAS204.
Power MC, Adar SD, Yanosky JD, Weuve J. 2016a. Exposure to air pollution as a potential contributor to cognitive function, cognitive decline, brain imaging, and dementia: a systematic review of epidemiologic research. Neurotoxicology 56:235–253, PMID: 27328897, 10.1016/j.neuro.2016.06.004.
Power MC, Schneider AL, Wruck L, Griswold M, Coker LH, Alonso A, 2016b. Life-course blood pressure in relation to brain volumes. Alzheimers Dement 12(8):890–899, PMID: 27139841, 10.1016/j.jalz.2016.03.012.
Power MC, Weisskopf MG, Alexeeff SE, Coull BA, Spiro A III, Schwartz J. 2011. Traffic-related air pollution and cognitive function in a cohort of older men. Environ Health Perspect 119(5):682–687, PMID: 21172758, 10.1289/ehp.1002767.
Raz L, Jayachandran M, Tosakulwong N, Lesnick TG, Wille SM, Murphy MC, et al. 2013. Thrombogenic microvesicles and white matter hyperintensities in postmenopausal women. Neurology 80(10):911–918, PMID: 23408873, 10.1212/WNL.0b013e3182840c9f.
Rosner B. 1983. Percentage points for a generalized ESD many-oulier procedure. Technometrics 25(2):165–172, 10.1080/00401706.1983.10487848.
Tzivian L, Dlugaj M, Winkler A, Weinmayr G, Hennig F, Fuks KB, et al. 2016. Long-term air pollution and traffic noise exposures and mild cognitive impairment in older adults: a cross-sectional analysis of the Heinz Nixdorf Recall Study. Environ Health Perspect 124(9):1361–1368, PMID: 26863687, 10.1289/ehp.1509824.
Wardlaw JM, Smith EE, Biessels GJ, Cordonnier C, Fazekas F, Frayne R, et al. 2013. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol 12(8):822–838, PMID: 23867200, 10.1016/S1474-4422(13)70124-8.
Weuve J, Proust-Lima C, Power MC, Gross AL, Hofer SM, Thiebaut R, et al. 2015. Guidelines for reporting methodological challenges and evaluating potential bias in dementia research. Alzheimers Dement 11(9):1098–1109, PMID: 26397878, 10.1016/j.jalz.2015.06.1885.
Wilker EH, Martinez-Ramirez S, Kloog I, Schwartz J, Mostofsky E, Koutrakis P, et al. 2016. Fine particulate matter, residential proximity to major roads, and markers of small vessel disease in a memory study population. J Alzheimers Dis 53(4):1315–1323, PMID: 27372639, 10.3233/JAD-151143.
Wilker EH, Preis SR, Beiser AS, Wolf PA, Au R, Kloog I, et al. 2015. Long-term exposure to fine particulate matter, residential proximity to major roads and measures of brain structure. Stroke 46(5):1161–1166, PMID: 25908455, 10.1161/STROKEAHA.114.008348.
Xu X, Ha SU, Basnet R. 2016. A review of epidemiological research on adverse neurological effects of exposure to ambient air pollution. Front Public Health 4:157, PMID: 27547751, 10.3389/fpubh.2016.00157.
Yanosky J, Paciorek C, Laden F, Hart J, Puett RC, Liao D, et al. 2014. Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors. Environ Health 13:63, PMID: 25097007, 10.1186/1476-069X-13-63.
Yanosky JD, Paciorek CJ, Schwartz J, Laden F, Puett R, Suh HH. 2008. Spatio-temporal modeling of chronic PM10 exposure for the Nurses’ Health Study. Atmos Environ (1994) 42(18):4047–4062, PMID: 19584946, 10.1016/j.atmosenv.2008.01.044.
Yanosky JD, Paciorek CJ, Suh HH. 2009. Predicting chronic fine and coarse particulate exposures using spatiotemporal models for the Northeastern and Midwestern United States. Environ Health Perspect 117(4):522–529, PMID: 19440489, 10.1289/ehp.11692.
EHP is pleased to present the abstracts from the 29th Annual Scientific Conference of the International Society for Environmental Epidemiology (ISEE), held in Sydney, Australia, 24–28 September 2017. The conference was hosted by The University of Sydney and cosponsored by the Woolcock Institute of Medical Research, with the theme “Healthy Places, Healthy People—Where Are the Connections?”