Koychev Ivan, Vaci Nemanja, Bilgel Murat, An Yang, Muniz Graciela Terrera, Wong Dean F, Gallacher John, Mogekhar Abhay, Albert Marilyn, Resnick Susan M
Department of Psychiatry University of Oxford Oxford UK.
Laboratory of Behavioral Neuroscience National Institute on Aging National Institutes of Health Baltimore Maryland.
Alzheimers Dement (Amst). 2020 Mar 22;12(1):e12019. doi: 10.1002/dad2.12019. eCollection 2020.
To test the hypothesis that among cognitively healthy individuals, distinct groups exist in terms of amyloid and phosphorylated-tau accumulation rates; that if rapid accumulator groups exist, their membership can be predicted by Alzheimer's disease (AD) risk factors, and that time points of significant increase in AD protein accumulation will be evident.
The analysis reports data from 263 individuals from the BIOCARD and 184 individuals from the Baltimore Longitudinal Study of Aging with repeated cerebrospinal fluid (CSF) and positron emission tomography (PET) sampling, respectively. We used latent class mixed-effect models to identify distinct classes of amyloid (CSF and PET) and p-Tau (CSF) accumulation rates and generalized additive modeling to investigate non-linear changes to AD biomarkers.
For both amyloid and p-Tau latent class models we confirmed the existence of two separate classes: accumulators and non-accumulators. The accumulator and non-accumulator groups differed significantly in terms of baseline AD protein levels and slope of change. ε4 carrier status and episodic memory predicted amyloid class membership. Non-linear models revealed time points of significant increase in the rate of amyloid and p-Tau accumulation whereby ε4 carrier status associated with earlier age at onset of rapid accumulation.
The current analysis demonstrates the existence of distinct classes of amyloid and p-Tau accumulators. Predictors of class membership were identified but the overall accuracy of the models was modest, highlighting the need for additional biomarkers that are sensitive to early disease phenotypes.
检验以下假设:在认知健康个体中,存在不同的淀粉样蛋白和磷酸化tau蛋白积累率分组;如果存在快速积累者分组,其成员可通过阿尔茨海默病(AD)风险因素预测,且AD蛋白积累显著增加的时间点将很明显。
该分析报告了分别来自BIOCARD的263名个体和巴尔的摩衰老纵向研究的184名个体的数据,他们分别进行了重复的脑脊液(CSF)和正电子发射断层扫描(PET)采样。我们使用潜在类别混合效应模型来识别淀粉样蛋白(CSF和PET)和p-Tau(CSF)积累率的不同类别,并使用广义相加模型来研究AD生物标志物的非线性变化。
对于淀粉样蛋白和p-Tau潜在类别模型,我们都证实存在两个不同的类别:积累者和非积累者。积累者和非积累者组在基线AD蛋白水平和变化斜率方面存在显著差异。ε4携带者状态和情景记忆可预测淀粉样蛋白类别成员。非线性模型揭示了淀粉样蛋白和p-Tau积累率显著增加的时间点,其中ε4携带者状态与快速积累开始的较早年龄相关。
当前分析表明存在不同类别的淀粉样蛋白和p-Tau积累者。已确定类别成员的预测因素,但模型的总体准确性一般,这突出表明需要对早期疾病表型敏感的其他生物标志物。