Analytical and clinical performance of eight Simoa and Lumipulse assays for automated measurement of plasma p-tau181 and p-tau217.

Background: Among the Alzheimer's disease (AD) biomarkers measured in blood, phosphorylated forms of tau (p-tau) have been shown to exhibit a particularly high diagnostic potential. Here, we performed a comprehensive method comparison study, followed by evaluation of the diagnostic performance of eight recent plasma p-tau immunoassays targeting different tau phosphorylation sites, different tau fragments, and that are measured by two distinct platforms.

Methods: We enrolled a cohort of 40 patients with AD at the stage of dementia (AD-dem) characterized by positive CSF A + T + profile, and a control group of 40 cognitively healthy participants (Control), to conduct a comprehensive method comparison for three plasma p-tau181 and five plasma p-tau217 assays run on the Simoa HD-X™ or Lumipulse G600II/G1200 platforms.

Defining benefit: Clinically and biologically meaningful outcomes in the next-generation Alzheimer's disease clinical care pathway.

To understand the potential benefits of emerging Alzheimer's disease (AD) therapies within and beyond clinical trial settings, there is a need to advance current outcome measurements into meaningful information relevant to all stakeholders. The relationship between the impact on disease biology and clinically measurable outcomes in cognition, function, and behavior must be considered when defining the meaningful benefit of early AD therapies. In this review, we discuss: (1) the lack of consideration for biomarkers in the current concept of meaningfulness in AD; (2) the lack of gold standards for determining minimal biologically and clinically important differences (MBCIDs) in AD trials; (3) how the treatment benefits of disease-modifying treatments are cumulative and increase over time; and (4) the different concepts of meaningfulness among key stakeholders.

A Machine Learning Model to Harmonize Volumetric Brain MRI Data for Quantitative Neuroradiological Assessment of Alzheimer Disease.

Purpose To extend a previously developed machine learning algorithm for harmonizing brain volumetric data of individuals undergoing neuroradiological assessment of Alzheimer disease not encountered during model training. Materials and Methods Neuroharmony is a recently developed method that uses image quality metrics (IQM) as predictors to remove scanner-related effects in brain-volumetric data using random forest regression. To account for the interactions between Alzheimer disease pathology and IQM during harmonization, the authors developed a multiclass extension of Neuroharmony for individuals with and without cognitive impairment.

The need for personalization when sharing results of amyloid imaging for Alzheimer's disease: Insights from a randomized experimental study.

Objective: To study information needs after receiving abnormal amyloid-PET results, and how individual characteristics moderate effects of different communication strategies on information recall.

Methods: In an online video-vignette experiment, seven vignettes each depicted a consultation of a physician sharing abnormal amyloid-PET results with a patient with Mild Cognitive Impairment(MCI), using different communication strategies. Healthy individuals (N = 1017; age 64 ± 8, 808(79 %) female), instructed to imagine themselves as the video-patient, viewed a randomly-assigned vignette and completed questionnaires to assess information needs and test moderation effects of gender, age, care-partner experience, health literacy, and coping.