Department of Epidemiology, Milken Institute School of Public Health, The George Washington University, Washington, District of Columbia, USA.
Zachry Department of Civil & Environmental Engineering, Texas A&M University, College Station, Texas, USA.
Environ Health Perspect. 2024 Jun;132(6):67010. doi: 10.1289/EHP13906. Epub 2024 Jun 26.
Evidence linking gaseous air pollution to late-life brain health is mixed.
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.
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.
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.
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.
将气态空气污染与晚年大脑健康联系起来的证据参差不齐。
我们探索了动脉粥样硬化风险社区(ARIC)研究参与者中暴露于气态污染物与脑磁共振成像(MRI)标志物之间的关联,同时关注暴露估计方法的影响和地点混杂因素。
我们考虑了在 1987-1989 年期间从美国四个地点招募的 1665 名符合条件的 ARIC 参与者的数据,这些参与者在第五次访问(2011-2013 年)中提供了有效的脑 MRI 数据。我们使用多种暴露估计方法,估计了参与者地址处 10 年(2001-2010 年)的一氧化碳(CO)、二氧化氮()、氮氧化物()以及 8 小时和 24 小时臭氧()的平均浓度。我们使用调整后的线性和逻辑回归模型,估计了污染物暴露与脑 MRI 结果(总体和区域体积;微出血、梗死、腔隙和严重白质高信号的存在)之间的特定地点关联。我们将特定地点的汇总关联与不考虑地点的分析进行了比较。
在不同的暴露估计方法中,特定地点的暴露分布差异很大。无论暴露、结果还是暴露估计方法如何,汇总分析的关联通常都没有统计学意义;点估计常常表明,较高的和较小的颞叶、深部灰质、海马、额叶和阿尔茨海默病特征感兴趣区的体积之间存在关联,较高的 CO 与较小的颞叶和额叶体积之间存在关联。不考虑研究地点的分析往往会产生显著的关联,有时关联的方向也不同。
不同的估计方法导致估计的空气污染浓度存在局部差异。尽管我们没有发现强有力的证据支持气态污染物对 MRI 可检测的大脑变化的影响,但点估计表明,较高的 CO、、和暴露与较小的区域脑体积之间存在关联。不调整位置分析空气污染与痴呆相关结果的研究可能会低估不确定性,并且容易受到混杂偏差的影响。https://doi.org/10.1289/EHP13906.