Quantitative Assessment of the Effect of Chronic Kidney Disease on Plasma P-Tau217 Concentrations.

Background And Objectives: Chronic kidney disease (CKD) is known to be associated with increased plasma phosphorylated tau217 (p-tau217) concentrations, potentially confounding the utility of plasma p-tau217 measurements as a marker of amyloid pathology in individuals with suspected Alzheimer disease (AD). In this study, we quantitatively investigate the relationship of plasma p-tau217 concentrations vs estimated glomerular filtration rate (eGFR) in individuals with CKD with and without amyloid pathology.

Methods: This was a retrospective examination of data from 2 observational cohorts from either the Mayo Clinic Study of Aging or the Alzheimer's Disease Research Center cohorts.

Association of plasma biomarkers of Alzheimer's pathology and neurodegeneration with gait performance in older adults.

Background: Declining gait performance is seen in aging individuals, due to neural and systemic factors. Plasma biomarkers provide an accessible way to assess evolving brain changes; non-specific neurodegeneration (NfL, GFAP) or evolving Alzheimer's disease (Aβ 42/40 ratio, P-Tau181).

Methods: In a population-based cohort of older adults, we evaluate the hypothesis that plasma biomarkers of neurodegeneration and Alzheimer's Disease pathology are associated with worse gait performance.

Lifetime risk and projected burden of dementia.

Understanding the lifetime risk of dementia can inform public health planning and improve patient engagement in prevention. Using data from a community-based, prospective cohort study (n = 15,043; 26.9% Black race, 55.

Longitudinal FDG-PET Metabolic Change Along the Lewy Body Continuum.

Importance: Although 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is a well-established cross-sectional biomarker of brain metabolism in dementia with Lewy bodies (DLB), the longitudinal change in FDG-PET has not been characterized.

Objective: To investigate longitudinal FDG-PET in prodromal DLB and DLB, including a subsample with autopsy data, and report estimated sample sizes for a hypothetical clinical trial in DLB.

Design, Setting, And Participants: Longitudinal case-control study with mean (SD) follow-up of 3.

Predicting cognitive decline from neuropsychiatric symptoms and Alzheimer's disease biomarkers: A machine learning approach to a population-based data.

Background: The aim of this study was to examine the potential added value of including neuropsychiatric symptoms (NPS) in machine learning (ML) models, along with demographic features and Alzheimer's disease (AD) biomarkers, to predict decline or non-decline in global and domain-specific cognitive scores among community-dwelling older adults.

Objective: To evaluate the impact of adding NPS to AD biomarkers on ML model accuracy in predicting cognitive decline among older adults.

Methods: The study was conducted in the setting of the Mayo Clinic Study of Aging, including participants aged ≥ 50 years with information on demographics (i.