Department of Psychology, University of Alberta, Edmonton, AB, Canada.
Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada.
J Alzheimers Dis. 2022;88(1):97-115. doi: 10.3233/JAD-215289.
Hippocampal atrophy is a well-known biomarker of neurodegeneration, such as that observed in Alzheimer's disease (AD). Although distributions of hippocampal volume trajectories for asymptomatic individuals often reveal substantial heterogeneity, it is unclear whether interpretable trajectory classes can be objectively detected and used for prediction analyses.
To detect and predict hippocampal trajectory classes in a computationally competitive context using established AD-related risk factors/biomarkers.
We used biomarker/risk factor and longitudinal MRI data in asymptomatic adults from the AD Neuroimaging Initiative (n = 351; Mean = 75 years; 48.7% female). First, we applied latent class growth analyses to left (LHC) and right (RHC) hippocampal trajectory distributions to identify distinct classes. Second, using random forest analyses, we tested 38 multi-modal biomarkers/risk factors for their relative importance in discriminating the lower (potentially elevated atrophy risk) from the higher (potentially reduced risk) class.
For both LHC and RHC trajectory distribution analyses, we observed three distinct trajectory classes. Three biomarkers/risk factors predicted membership in LHC and RHC lower classes: male sex, higher education, and lower plasma Aβ1-42. Four additional factors selectively predicted membership in the lower LHC class: lower plasma tau and Aβ1-40, higher depressive symptomology, and lower body mass index.
Data-driven analyses of LHC and RHC trajectories detected three classes underlying the heterogeneous distributions. Machine learning analyses determined three common and four unique biomarkers/risk factors discriminating the higher and lower LHC/RHC classes. Our sequential analytic approach produced evidence that the dynamics of preclinical hippocampal trajectories can be predicted by AD-related biomarkers/risk factors from multiple modalities.
海马体萎缩是神经退行性变的一个众所周知的生物标志物，如阿尔茨海默病（AD）中观察到的那样。尽管无症状个体的海马体体积轨迹分布通常显示出很大的异质性，但尚不清楚是否可以客观地检测到可解释的轨迹类别并将其用于预测分析。
使用已建立的与 AD 相关的风险因素/生物标志物，在计算上具有竞争力的背景下检测和预测海马体轨迹类别。
我们使用 AD 神经影像学倡议（n=351；Mean=75 岁；48.7%女性）中无症状成年人的生物标志物/风险因素和纵向 MRI 数据。首先，我们应用潜在类别增长分析来识别左（LHC）和右（RHC）海马体轨迹分布中的不同类别。其次，使用随机森林分析，我们测试了 38 种多模态生物标志物/风险因素在区分低值（潜在高萎缩风险）和高值（潜在低风险）类别方面的相对重要性。
对于 LHC 和 RHC 轨迹分布分析，我们观察到三个不同的轨迹类别。三个生物标志物/风险因素预测 LHC 和 RHC 低值类别的成员身份：男性、较高的教育程度和较低的血浆 Aβ1-42。另外四个因素选择性地预测了 LHC 低值类别的成员身份：较低的血浆 tau 和 Aβ1-40、较高的抑郁症状和较低的体重指数。
对 LHC 和 RHC 轨迹的数据分析检测到三个潜在异质分布的类别。机器学习分析确定了三个共同的和四个独特的生物标志物/风险因素，区分了较高和较低的 LHC/RHC 类别。我们的顺序分析方法提供了证据，表明 AD 相关生物标志物/风险因素可以从多个模态预测临床前海马体轨迹的动态。
