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Abstract：
BACKGROUND: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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).
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
背景：利用阿尔茨海默病（AD）成像生物标志物和纵向认知数据可能使我们能够在体内建立AD病理的认知弹性（CR）证据。这里,我们应用了潜在类混合建模，适应性，基线年龄,和淀粉样蛋白的神经影像学生物标志物，tau和神经变性，对认知未受损的老年人样本进行识别，以确定CR的纵向轨迹。
方法：我们确定了200名哈佛脑衰老研究（HABS）参与者（平均年龄=71.89岁，SD=9.41年，59％的女性）在基线时认知未受损，在单个淀粉样蛋白PET后进行2个或更多个时间点的认知评估，tau-PET和结构MRI。我们检查了以纵向认知为因变量和基线时间的潜在类混合模型，基线年龄,性别,新皮质Aβ，entorhinaltau,调整海马体积作为自变量。然后，我们从一个有利的模型中检查了识别出的亚组中CR相关因子的组差异。最后,我们将我们喜欢的模型应用于阿尔茨海默病神经影像学计划的数据集(ADNI;n=160，平均年龄=73.9岁，SD=7.6年，60%女性）。
结果：偏爱模型确定了3个潜在亚组，我们将其标记为正常（HABS样本的71％），弹性(22.5%)和下降(6.5%)亚组。弹性亚组表现出更高的基线认知表现和稳定的认知斜率。他们与其他群体的区别在于更高水平的言语智力和过去的认知活动。在ADNI,该模型确定了一个更大的正常亚组(88.1%)，较小的弹性亚组（6.3％）和认知基线较低的下降组（5.6％）。
结论：这些发现证明了数据驱动方法在临床前AD中识别纵向CR组的价值。有了这样的方法,我们根据以前的文献确定了一个反映预期特征的CR亚组，更高水平的言语智力和过去的认知活动。
方法：我们确定了200名哈佛脑衰老研究（HABS）参与者（平均年龄=71.89岁，SD=9.41年，59％的女性）在基线时认知未受损，在单个淀粉样蛋白PET后进行2个或更多个时间点的认知评估，tau-PET和结构MRI。我们检查了以纵向认知为因变量和基线时间的潜在类混合模型，基线年龄,性别,新皮质Aβ，entorhinaltau,调整海马体积作为自变量。然后，我们从一个有利的模型中检查了识别出的亚组中CR相关因子的组差异。最后,我们将我们喜欢的模型应用于阿尔茨海默病神经影像学计划的数据集(ADNI;n=160，平均年龄=73.9岁，SD=7.6年，60%女性）。
结果：偏爱模型确定了3个潜在亚组，我们将其标记为正常（HABS样本的71％），弹性(22.5%)和下降(6.5%)亚组。弹性亚组表现出更高的基线认知表现和稳定的认知斜率。他们与其他群体的区别在于更高水平的言语智力和过去的认知活动。在ADNI,该模型确定了一个更大的正常亚组(88.1%)，较小的弹性亚组（6.3％）和认知基线较低的下降组（5.6％）。
结论：这些发现证明了数据驱动方法在临床前AD中识别纵向CR组的价值。有了这样的方法,我们根据以前的文献确定了一个反映预期特征的CR亚组，更高水平的言语智力和过去的认知活动。
