Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
Alzheimers Res Ther. 2024 Jul 3;16(1):148. doi: 10.1186/s13195-024-01510-y.
Leveraging Alzheimer's disease (AD) imaging biomarkers and longitudinal cognitive data may allow us to establish evidence of cognitive resilience (CR) to AD pathology in-vivo. Here, we applied latent class mixture modeling, adjusting for sex, baseline age, and neuroimaging biomarkers of amyloid, tau and neurodegeneration, to a sample of cognitively unimpaired older adults to identify longitudinal trajectories of CR.
We identified 200 Harvard Aging Brain Study (HABS) participants (mean age = 71.89 years, SD = 9.41 years, 59% women) who were cognitively unimpaired at baseline with 2 or more timepoints of cognitive assessment following a single amyloid-PET, tau-PET and structural MRI. We examined latent class mixture models with longitudinal cognition as the dependent variable and time from baseline, baseline age, sex, neocortical Aβ, entorhinal tau, and adjusted hippocampal volume as independent variables. We then examined group differences in CR-related factors across the identified subgroups from a favored model. Finally, we applied our favored model to a dataset from the Alzheimer's Disease Neuroimaging Initiative (ADNI; n = 160, mean age = 73.9 years, SD = 7.6 years, 60% women).
The favored model identified 3 latent subgroups, which we labelled as Normal (71% of HABS sample), Resilient (22.5%) and Declining (6.5%) subgroups. The Resilient subgroup exhibited higher baseline cognitive performance and a stable cognitive slope. They were differentiated from other groups by higher levels of verbal intelligence and past cognitive activity. In ADNI, this model identified a larger Normal subgroup (88.1%), a smaller Resilient subgroup (6.3%) and a Declining group (5.6%) with a lower cognitive baseline.
These findings demonstrate the value of data-driven approaches to identify longitudinal CR groups in preclinical AD. With such an approach, we identified a CR subgroup who reflected expected characteristics based on previous literature, higher levels of verbal intelligence and past cognitive activity.
利用阿尔茨海默病（AD）成像生物标志物和纵向认知数据，我们可能可以在体内建立 AD 病理学认知弹性（CR）的证据。在这里，我们应用潜在类别混合建模，调整性别、基线年龄以及淀粉样蛋白、tau 和神经退行性变的神经影像学生物标志物，对认知正常的老年人样本进行分析，以确定 CR 的纵向轨迹。
我们确定了 200 名哈佛衰老大脑研究（HABS）参与者（平均年龄 71.89 岁，标准差 9.41 岁，59%为女性），他们在基线时认知正常，在单次淀粉样蛋白-PET、tau-PET 和结构 MRI 后有 2 个或更多时间点的认知评估。我们使用纵向认知作为因变量，以从基线开始的时间、基线年龄、性别、新皮质 Aβ、内嗅皮质 tau 和调整后的海马体积作为自变量，检查潜在类别混合模型。然后，我们检查了从首选模型中确定的亚组中与 CR 相关的因素的组间差异。最后，我们将我们的首选模型应用于阿尔茨海默病神经影像学倡议（ADNI；n=160，平均年龄 73.9 岁，标准差 7.6 岁，60%为女性）的数据集。
首选模型确定了 3 个潜在亚组，我们将其标记为正常（HABS 样本的 71%）、有弹性（22.5%）和下降（6.5%）亚组。有弹性的亚组表现出更高的基线认知表现和稳定的认知斜率。他们通过更高的语言智力和过去的认知活动水平与其他群体区分开来。在 ADNI 中，该模型确定了一个更大的正常亚组（88.1%）、一个更小的有弹性亚组（6.3%）和一个下降组（5.6%），其认知基线较低。
这些发现表明，采用数据驱动的方法可以识别临床前 AD 中的纵向 CR 组。通过这种方法，我们确定了一个 CR 亚组，该亚组反映了基于先前文献的预期特征，即更高的语言智力和过去的认知活动水平。
Zotero 插件
