Person:

Hart, Jaime

Background: Exposure to air pollution has been implicated in a number of adverse health outcomes and the effect of particulate matter (PM) on the brain is beginning to be recognized. Yet, no prospective study has examined the association between PM and risk of Parkinson Disease. Thus, our goal was assess if exposure to particulate matter air pollution is related to risk of Parkinson’s disease (PD) in the Nurses’ Health Study (NHS), a large prospective cohort of women. Methods: Cumulative average exposure to different size fractions of PM up to 2 years before the onset of PD, was estimated using a spatio-temporal model by linking each individual’s places of residence throughout the study with location-specific air pollution levels. We prospectively followed 115,767 women in the NHS, identified 508 incident PD cases and used multivariable Cox proportional hazards models to estimate the risk of PD associated with each size fraction of PM independently. Results: In models adjusted for age in months, smoking, region, population density, caffeine and ibuprofen intake, we observed no statistically significant associations between exposure to air pollution and PD risk. The relative risk (RR) comparing the top quartile to the bottom quartile of PM exposure was 0.99 (95% Confidence Intervals (CI): 0.84,1.16) for PM10 (≤10 microns in diameter), 1.08 (95% CI: 0.81, 1.45) for PM2.5 (≤2.5 microns in diameter), and 0.92 (95% CI: 0.71, 1.19) for PM10–2.5 (2.5 to 10 microns in diameter). Conclusions: In this study, we found no evidence that exposure to air pollution is a risk factor for PD.

Background: Ambient air pollution exposures have been frequently linked to cardiovascular disease (CVD) morbidity and mortality. However, less is known about the populations most susceptible to these adverse effects. Methods and Results: We assessed the associations of long‐term particulate matter (PM) exposures with incident CVD in a nationwide cohort of 114 537 women in the Nurses’ Health Study, and performed analyses to identify subpopulations at the greatest risk. Residential address level time‐varying monthly exposures to PM 2.5, PM 10, and PM 2.5 to 10 microns in diameter were estimated from spatio‐temporal prediction models. In multivariable models, increases in all size fractions of PM were associated with small, but not statistically significant, increased risks of total CVD, coronary heart disease, and stroke. PM‐associated CVD risks were statistically significantly higher among women with diabetes as compared to those without (P‐for‐interaction <0.0001 for PM 10 and PM 2.5 and 0.007 for PM 2.5 to 10). For each 10 μg/m3 increase in 12‐month average PM 2.5, PM 2.5 to 10, and PM 10, the multivariable adjusted hazard ratios were 1.44 (95% CI: 1.23 to 1.68), 1.17 (95% CI: 1.05 to 1.30), and 1.19 (95% CI: 1.10 to 1.28) among women with diabetes. There were also suggestions of higher risks among older (≥70 years) women, the obese, and those living in the Northeast and South. Smoking status and family history did not consistently modify the association between PM and CVD, and risks were most elevated with exposures in the previous 12 months. Conclusions: In this nationwide cohort, women with diabetes were identified as the subpopulation most sensitive to the adverse cardiovascular health effects of PM.

Background: High mammographic density is a strong, well-established breast cancer risk factor. Three studies conducted in various smaller geographic settings reported inconsistent findings between air pollution and mammographic density. We assessed whether particulate matter (PM) exposures (PM2.5, PM2.5–10, and PM10) and distance to roadways were associated with mammographic density among women residing across the United States. Methods: The Nurses’ Health Studies are prospective cohorts for whom a subset has screening mammograms from the 1990s (interquartile range 1990–1999). PM was estimated using spatio-temporal models linked to residential addresses. Among 3258 women (average age at mammogram 52.7 years), we performed multivariable linear regression to assess associations between square-root-transformed mammographic density and PM within 1 and 3 years before the mammogram. For linear regression estimates of PM in relation to untransformed mammographic density outcomes, bootstrapped robust standard errors are used to calculate 95% confidence intervals (CIs). Analyses were stratified by menopausal status and region of residence. Results: Recent PM and distance to roadways were not associated with mammographic density in premenopausal women (PM2.5 within 3 years before mammogram β = 0.05, 95% CI –0.16, 0.27; PM2.5–10 β = 0, 95%, CI –0.15, 0.16; PM10 β = 0.02, 95% CI –0.10, 0.13) and postmenopausal women (PM2.5 within 3 years before mammogram β = –0.05, 95% CI –0.27, 0.17; PM2.5–10 β = –0.01, 95% CI –0.16, 0.14; PM10 β = –0.02, 95% CI –0.13, 0.09). Largely null associations were observed within regions. Suggestive associations were observed among postmenopausal women in the Northeast (n = 745), where a 10-μg/m3 increase in PM2.5 within 3 years before the mammogram was associated with 3.4 percentage points higher percent mammographic density (95% CI –0.5, 7.3). Conclusions: These findings do not support that recent PM or roadway exposures influence mammographic density. Although PM was largely not associated with mammographic density, we cannot rule out the role of PM during earlier exposure time windows and possible associations among northeastern postmenopausal women. Electronic supplementary material The online version of this article (doi:10.1186/s13058-017-0915-5) contains supplementary material, which is available to authorized users.