What do data-driven Alzheimer's disease subtypes tell us about white matter pathology and clinical progression?
What do data-driven Alzheimer's disease subtypes tell us about white matter pathology and clinical progression?
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数据驱动的阿尔茨海默病亚型告诉我们有关白质病理学和临床进展的哪些信息？
DOI：
10.1002/alz.054028
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Chen H
中科院分区：
文献类型：
--
作者：
Chen H
背景阿尔茨海默病(AD)的进展是异质性的，不同的脑区在疾病期间的不同时间表现出病理变化。灰质(GM)体积变化的不同序列定义了基于成像的亚型，可能代表不同的疾病起源、途径或严重程度。此外，随着年龄的增长，白质高信号(WMH)的体积也与AD有关。我们使用机器学习方法来估计三种类型的AD轨迹，并评估它们的WMH负荷和纵向预后。方法使用亚型和阶段推断(Constaint；Young et al.，https://doi.org/10.1038/s41467-018-05892-0)从795名受试者的横断面GM体积中估计数据驱动的疾病进展亚型(从ADNI1/GO/2开始，基线3T磁共振扫描使用freesurfer5.1处理，通过数据质量控制)。根据额外的质量控制，使用贝叶斯模型选择方法BAMOS(sudre et al.，https://doi.org/10.1109/TMI.2015.2419072))计算了575名ADNI受试者的WMH体积，来自三个诊断组：健康对照组(HC；n=181)、轻度认知障碍(MCI；n=310)和阿尔茨海默病(AD；n=85)。结果SuStaIn确定了三种具有不同萎缩模式的亚型：典型(萎缩始于海马体和杏仁核)、皮质(萎缩始于颞叶，然后是扣带状核和脑岛)和皮质下(萎缩始于苍白球、壳核和尾状核)(图1)。皮质下亚型的参与者表现出临床进展到MCI或AD的速度明显较慢(图2，表1)。此外，皮质下亚型受试者的WMH中位数较低(图3a)，这是在诊断组中观察到的模式(图3b)。结论被归类为皮质下亚型的受试者最不可能出现与神经变性相关的白质结构变化，临床转化率最慢。虽然皮质下亚型的晚期海马受累可能解释了较轻的临床症状和较慢的疾病进展，但与该亚型轨迹相关的较低WMH的原因尚不清楚。还需要进一步研究WMH和AD萎缩亚型之间的联系。
BackgroundThe progression of Alzheimer’s Disease (AD) is heterogeneous with different brain regions manifesting pathology at different times during the disease. Differential sequences of grey matter (GM) volume changes, defining imaging‐based subtypes, may represent alternative disease origins, pathways or severity. Additionally, volumes of white matter hyperintensities (WMH), which increase with age, are associated with AD. In this study we used a machine learning method to estimate three types of AD trajectories and assessed their WMH load and longitudinal prognosis.MethodSubtype and Stage Inference (SuStaIn; Young et al., https://doi.org/10.1038/s41467‐018‐05892‐0) was used to estimate data‐driven disease progresion subtypes from cross‐sectional GM volumes in 795 subjects (from ADNI1/GO/2 with baseline 3T MRI scans processed with FreeSurfer 5.1 passing data quality control). Following additional quality control WMH volumes for 575 of these ADNI subjects were calculated using a Bayesian Model Selection method BaMoS (Sudre et al., https://doi.org/10.1109/TMI.2015.2419072) from three diagnostic groups: Healthy Controls (HC; n=181), Mild Cognitive Impairment (MCI; n=310) and Alzheimer’s Disease (AD; n=85). The association between SuStaIn subtypes and WMH and diagnostic conversion rates were evaluated using multiple pairwise comparisons and survival analysis, respectively.ResultSuStaIn identified three subtypes with distinct atrophy patterns: typical (atrophy begins in hippocampus and amygdala), cortical (atrophy starts in the temporal lobe, followed by cingulate and insula) and subcortical (atrophy begins in pallidum, putamen and caudate) (Figure 1). Participants with the subcortical subtype exhibited a significantly slower rate of clinical progression to MCI or AD (Figure 2, Table 1). Furthemore, subjects with the subcortical subtype showed lower median WMH (Figure 3a), a pattern observed across the diagnostic groups (Figure 3b).ConclusionParticipants classified as the subcortical subtype were least likely to have white matter structural changes associated with neurodegeneration and displayed the slowest rate of clinical conversion. While late hippocampal involvement in the subcortical subtype may explain less severe clinical symptoms and slower disease progression, the reason for lower WMH associated with this subtype trajectory is unknown. Further studies are needed to investigate the link between WMH and AD atrophy subtypes.