Association of Gaseous Ambient Air Pollution and Dementia-Related Neuroimaging Markers in the ARIC Cohort, Comparing Exposure Estimation Methods and Confounding by Study Site

Katie M. Lynch, Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA.
Erin E. Bennett, Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA.
Qi Ying, Zachry Department of Civil & Environmental Engineering, Texas A&M University, College Station, Texas, USA.
Eun Sug Park, Texas A&M Transportation Institute, Texas A&M University System, College Station, Texas, USA.
Xiaohui Xu, Department of Epidemiology & Biostatistics, Texas A&M Health Science Center School of Public Health, College Station, Texas, USA.
Richard L. Smith, Department of Statistics and Operations Research, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
James D. Stewart, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Duanping Liao, Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania, USA.
Joel D. Kaufman, Departments of Environmental & Occupational Health Sciences, Medicine, and Epidemiology, University of Washington, Seattle, Washington, USA.
Eric A. Whitsel, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
Melinda C. Power, Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA.

Document Type

Journal Article

Publication Date

6-1-2024

Journal

Environmental health perspectives

Volume

132

Issue

6

DOI

10.1289/EHP13906

Abstract

BACKGROUND: Evidence linking gaseous air pollution to late-life brain health is mixed. OBJECTIVE: We explored associations between exposure to gaseous pollutants and brain magnetic resonance imaging (MRI) markers among Atherosclerosis Risk in Communities (ARIC) Study participants, with attention to the influence of exposure estimation method and confounding by site. METHODS: We considered data from 1,665 eligible ARIC participants recruited from four US sites in the period 1987-1989 with valid brain MRI data from Visit 5 (2011-2013). We estimated 10-y (2001-2010) mean carbon monoxide (CO), nitrogen dioxide (), nitrogen oxides (), and 8- and 24-h ozone () concentrations at participant addresses, using multiple exposure estimation methods. We estimated site-specific associations between pollutant exposures and brain MRI outcomes (total and regional volumes; presence of microhemorrhages, infarcts, lacunes, and severe white matter hyperintensities), using adjusted linear and logistic regression models. We compared meta-analytically combined site-specific associations to analyses that did not account for site. RESULTS: Within-site exposure distributions varied across exposure estimation methods. Meta-analytic associations were generally not statistically significant regardless of exposure, outcome, or exposure estimation method; point estimates often suggested associations between higher and smaller temporal lobe, deep gray, hippocampal, frontal lobe, and Alzheimer disease signature region of interest volumes and between higher CO and smaller temporal and frontal lobe volumes. Analyses that did not account for study site more often yielded significant associations and sometimes different direction of associations. DISCUSSION: Patterns of local variation in estimated air pollution concentrations differ by estimation method. Although we did not find strong evidence supporting impact of gaseous pollutants on brain changes detectable by MRI, point estimates suggested associations between higher exposure to CO, , and and smaller regional brain volumes. Analyses of air pollution and dementia-related outcomes that do not adjust for location likely underestimate uncertainty and may be susceptible to confounding bias. https://doi.org/10.1289/EHP13906.

Department

Epidemiology

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