Background: Blood-based biomarkers, especially P-tau217, have been gaining interest as diagnostic tools to measure Alzheimer disease (AD) pathology.
Methods: We developed a plasma P-tau217 chemiluminescent immunoassay using 4G10E2 and IBA493 as antibodies, a synthetic tau peptide as calibrator, and the Quanterix SP-X imager. Analytical validation performed in a College of American Pathologists-accredited CLIA laboratory involved multiple kit lots, operators, timepoints, and imagers.
Introduction: Alzheimer's disease is partially characterized by the progressive accumulation of aggregated tau-containing neurofibrillary tangles. Although the association between accumulated tau, neurodegeneration, and cognitive decline is critical for disease understanding and clinical trial design, we still lack robust tools to predict individualized trajectories of tau accumulation. Our objective was to assess whether brain imaging biomarkers of flortaucipir-positron emission tomography (PET), in combination with clinical and genomic measures, could predict future pathological tau accumulation.
View Article and Find Full Text PDFFibrillar tau gradually progresses in the brain during the course of Alzheimer's disease (AD). However, the contribution of tau accumulation in a given brain region to decline in different cognitive domains and thus phenotypic heterogeneity in AD remains unclear. Here, we leveraged the functional connectome to link the locality of tau accumulation to domain-specific cognitive impairment.
View Article and Find Full Text PDFIntroduction: Tau-positron emission tomography (PET) outcome data of patients with Alzheimer's disease (AD) cannot currently be meaningfully compared or combined when different tracers are used due to differences in tracer properties, instrumentation, and methods of analysis.
Methods: Using head-to-head data from five cohorts with tau PET radiotracers designed to target tau deposition in AD, we tested a joint propagation model (JPM) to harmonize quantification (units termed "CenTauR" [CTR]). JPM is a statistical model that simultaneously models the relationships between head-to-head and anchor point data.