Younan Diana, Wang Xinhui, Casanova Ramon, Barnard Ryan, Gaussoin Sarah A, Saldana Santiago, Petkus Andrew J, Beavers Daniel P, Resnick Susan M, Manson JoAnn E, Serre Marc L, Vizuete William, Henderson Victor W, Sachs Bonnie C, Salinas Joel, Gatz Margaret, Espeland Mark A, Chui Helena C, Shumaker Sally A, Rapp Stephen R, Chen Jiu-Chiuan
From the Departments of Preventive Medicine (D.Y., J.C.C) and Neurology (X.W., A.J.P., H.C.C., J.-C.C.) and the Center for Economic and Social Research (M.G.), University of Southern California, Los Angeles; Departments of Biostatistics and Data Science (R.C., R.B., S.A.G., S.S., D.P.B., M.A.E.), Psychiatry and Behavioral Medicine (S.R.R.), Social Sciences & Health Policy (S.A.S., S.R.R.), and Neurology (B.C.S.), Wake Forest School of Medicine, Winston-Salem, NC; Laboratory of Behavioral Neuroscience (S.M.R.), National Institute on Aging, Baltimore, MD; Department of Environmental Sciences and Engineering (M.L.S., W.V.), University of North Carolina, Chapel Hill; Departments of Health Research and Policy (Epidemiology) and Neurology and Neurological Sciences (V.W.H.), Stanford University, CA; Department of Medicine (J.E.M.), Brigham and Women's Hospital, Harvard Medical School, Boston, MA; and Center for Cognitive Neurology, Department of Neurology (J.S.), New York University Grossman School of Medicine, New York.
Neurology. 2021 Feb 22;96(8):e1190-e1201. doi: 10.1212/WNL.0000000000011149.
To examine whether late-life exposure to PM (particulate matter with aerodynamic diameters <2.5 µm) contributes to progressive brain atrophy predictive of Alzheimer disease (AD) using a community-dwelling cohort of women (age 70-89 years) with up to 2 brain MRI scans (MRI-1, 2005-2006; MRI-2, 2010-2011).
AD pattern similarity (AD-PS) scores, developed by supervised machine learning and validated with MRI data from the Alzheimer's Disease Neuroimaging Initiative, were used to capture high-dimensional gray matter atrophy in brain areas vulnerable to AD (e.g., amygdala, hippocampus, parahippocampal gyrus, thalamus, inferior temporal lobe areas, and midbrain). Using participants' addresses and air monitoring data, we implemented a spatiotemporal model to estimate 3-year average exposure to PM preceding MRI-1. General linear models were used to examine the association between PM and AD-PS scores (baseline and 5-year standardized change), accounting for potential confounders and white matter lesion volumes.
For 1,365 women 77.9 ± 3.7 years of age in 2005 to 2006, there was no association between PM and baseline AD-PS score in cross-sectional analyses (β = -0.004; 95% confidence interval [CI] -0.019 to 0.011). Longitudinally, each interquartile range increase of PM (2.82 µg/m) was associated with increased AD-PS scores during the follow-up, equivalent to a 24% (hazard ratio 1.24, 95% CI 1.14-1.34) increase in AD risk over 5 years (n = 712, age 77.4 ± 3.5 years). This association remained after adjustment for sociodemographics, intracranial volume, lifestyle, clinical characteristics, and white matter lesions and was present with levels below US regulatory standards (<12 µg/m).
Late-life exposure to PM is associated with increased neuroanatomic risk of AD, which may not be explained by available indicators of cerebrovascular damage.
利用一个社区居住的老年女性队列(年龄70 - 89岁),她们有多达两次脑部磁共振成像扫描(MRI - 1,2005 - 2006年;MRI - 2,2010 - 2011年),研究晚年暴露于细颗粒物(空气动力学直径<2.5 µm的颗粒物,PM)是否会导致预测阿尔茨海默病(AD)的进行性脑萎缩。
通过监督机器学习开发并经阿尔茨海默病神经影像倡议组织的MRI数据验证的AD模式相似性(AD - PS)评分,用于捕捉易患AD的脑区(如杏仁核、海马体、海马旁回、丘脑、颞叶下部区域和中脑)的高维灰质萎缩。利用参与者的住址和空气监测数据,我们实施了一个时空模型来估计在MRI - 1之前3年的PM平均暴露量。使用一般线性模型来检验PM与AD - PS评分(基线和5年标准化变化)之间的关联,同时考虑潜在的混杂因素和白质病变体积。
对于2005年至2006年年龄为77.9±3.7岁的1365名女性,在横断面分析中,PM与基线AD - PS评分之间无关联(β = -0.004;95%置信区间[CI] -0.019至0.011)。纵向来看,PM每增加一个四分位数间距(2.82 µg/m³)与随访期间AD - PS评分增加相关,相当于5年内AD风险增加24%(风险比1.24),95% CI 1.14至1.34)(n = 712,年龄77.4±3.5岁)。在对社会人口统计学、颅内体积生活方式临床特征和白质病变进行调整后,这种关联仍然存在,并且在低于美国监管标准(<12 µg/m³)的水平时也存在。
晚年暴露于PM与AD的神经解剖学风险增加相关,这可能无法用现有的脑血管损伤指标来解释。