Alzheimer’s Prevention Initiative

Variants in APOE and PSEN1 (encoding apolipoprotein E and presenilin 1, respectively) alter the risk of Alzheimer’s disease. We previously reported a delay of cognitive impairment in a person with autosomal dominant Alzheimer’s disease caused by the PSEN1^E280A variant who also had two copies of the apolipoprotein E3 Christchurch variant (APOE3^Ch). Heterozygosity for the APOE3^Ch variant may influence the age at which the onset of cognitive impairment occurs. We assessed this hypothesis in a population in which the PSEN1^E280A variant is prevalent.

Introduction: Plasma-measured tau phosphorylated at threonine 217 (p-tau217) is a potential non-invasive biomarker of Alzheimer’s disease (AD). We investigated whether plasma p-tau217 predicts subsequent cognition and positron emission tomography (PET) markers of pathology in autosomal dominant AD. Methods: We analyzed baseline levels of plasma p-tau217 and its associations with amyloid PET, tau PET, and word list delayed recall measured 7.61 years later in non-demented age- and education-matched presenilin-1 E280A carriers (n = 24) and non-carrier (n = 20) family members. Results: Carriers had higher plasma p-tau217 levels than non-carriers. Baseline plasma p-tau217 was associated with subsequent amyloid and tau PET pathology levels and cognitive function. Discussion: Our findings suggest that plasma p-tau217 predicts subsequent brain pathological burden and memory performance in presenilin-1 E280A carriers. These results provide support for plasma p-tau217 as a minimally invasive diagnostic and prognostic biomarker for AD, with potential utility in clinical practice and trials. Highlights: Non-demented presenilin-1 E280A carriers have higher plasma tau phosphorylated at threonine 217 (p-tau217) than do age-matched non-carriers. Higher baseline p-tau217 is associated with greater future amyloid positron emission tomography (PET) pathology burden. Higher baseline p-tau217 is associated with greater future tau PET pathology burden. Higher baseline p-tau217 is associated with worse future memory performance. © 2022 the Alzheimer’s Association.

Introduction: Multiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis. We accordingly developed and validated a deep learning model as a harmonization strategy. Method: A Residual Inception Encoder-Decoder Neural Network was developed to harmonize images between amyloid PET image pairs made with Pittsburgh Compound-B and florbetapir tracers. The model was trained using a dataset with 92 subjects with 10-fold cross validation and its generalizability was further examined using an independent external dataset of 46 subjects. Results: Significantly stronger between-tracer correlations (P <.001) were observed after harmonization for both global amyloid burden indices and voxel-wise measurements in the training cohort and the external testing cohort. Discussion: We proposed and validated a novel encoder-decoder based deep model to harmonize amyloid PET imaging data from different tracers. Further investigation is ongoing to improve the model and apply to additional tracers.