Habes Mohamad, Erus Guray, Toledo Jon B, Bryan Nick, Janowitz Deborah, Doshi Jimit, Völzke Henry, Schminke Ulf, Hoffmann Wolfgang, Grabe Hans J, Wolk David A, Davatzikos Christos
Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA.
Department of Neurology and Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA.
Alzheimers Dement (Amst). 2018 Mar 5;10:278-284. doi: 10.1016/j.dadm.2018.02.002. eCollection 2018.
We sought to investigate associations of regional white matter hyperintensities (WMHs) within white matter (WM) tracts with cardiovascular risk and brain aging-related atrophy throughout adulthood in the general population, leveraging state of the art pattern analysis methods.
We analyzed a large sample (n = 2367) from the Study of Health in Pomerania, Germany (range 20-90 years). WMHs were automatically segmented on T1-weighted and fluid-attenuated inversion recovery magnetic resonance images, and WMH volumes were calculated in WM regions defined using the John Hopkins University WM tractography atlas. Regions with the highest average WMH volume were selected. We calculated a subject-specific index, Spatial Pattern of Alteration for Recognition of Brain Aging, to measure age-related atrophy patterns. The Framingham cardiovascular disease risk score summarized the individual cardiovascular risk profile. We used structural equation models, independently for each region, using Spatial Pattern of Alteration for Recognition of Brain Aging as a dependent variable, age as an independent variable, and cardiovascular disease risk score and regional WMH volumes as mediators.
Selected 12 WM regions included 75% of the total WMH burden in average. Structural equation models showed that the age effect on Spatial Pattern of Alteration for Recognition of Brain Aging was mediated by WMHs to a different extent in the superior frontal WM, anterior corona radiata, inferior frontal WM, superior corona radiata, superior longitudinal fasciculus, middle temporal WM, posterior corona radiata, superior parietal WM, splenium of corpus callosum, posterior thalamic radiation, and middle occipital WM (variance explained between 2.8% and 10.3%, < .0001 Bonferroni corrected), but not in precentral WM.
Our results indicate that WMHs, in most WM tracts, might accelerate the brain aging process throughout adulthood in the general population as a result of vascular risk factors, but also independent of them. Preventive strategies against WMHs (such as controlling vascular risk factors or microglia depletion) could delay brain aging.
我们试图利用先进的模式分析方法，研究普通人群成年期白质（WM）束内区域白质高信号（WMHs）与心血管风险及脑老化相关萎缩之间的关联。
我们分析了来自德国波美拉尼亚健康研究的一个大样本（n = 2367）（年龄范围20 - 90岁）。在T1加权和液体衰减反转恢复磁共振图像上自动分割WMHs，并在使用约翰霍普金斯大学WM纤维束图谱定义的WM区域计算WMH体积。选择平均WMH体积最高的区域。我们计算了一个个体特异性指数，即用于识别脑老化的改变空间模式，以测量与年龄相关的萎缩模式。弗雷明汉心血管疾病风险评分总结了个体的心血管风险概况。我们使用结构方程模型，针对每个区域独立进行分析，将用于识别脑老化的改变空间模式作为因变量，年龄作为自变量，心血管疾病风险评分和区域WMH体积作为中介变量。
选定的12个WM区域平均包含了总WMH负担的75%。结构方程模型显示，年龄对用于识别脑老化的改变空间模式的影响在额上叶WM、放射冠前部、额下叶WM、放射冠上部、上纵束、颞中叶WM、放射冠后部、顶上叶WM、胼胝体压部、丘脑后辐射和枕中叶WM中，在不同程度上由WMHs介导（解释方差在2.8%至10.3%之间，经Bonferroni校正后P <.0001），但在中央前回WM中并非如此。
我们的结果表明，在大多数WM束中，WMHs可能由于血管危险因素，也可能独立于这些因素，在普通人群成年期加速脑老化过程。针对WMHs的预防策略（如控制血管危险因素或清除小胶质细胞）可能会延缓脑老化。
