Cerebrospinal fluid proteomics for predictive assessment of Alzheimer's Disease risk.

ABSTRACT: Alzheimer's disease (AD) involves early molecular changes beyond amyloid-β (Aβ) and tau, that create heterogeneous disease biology, giving rise to variable disease initiation and highly variable longitudinal trajectories. Accurately predicting trajectories is vital for design of clinical trials and for clinical care, yet current CSF and PET biomarkers provide limited predictive capabilities despite their excellent diagnostic value. We performed CSF proteomics using tandem-mass-tag mass spectrometry in 1,104 ADNI participants with extensive longitudinal assessments. Machine learning-derived protein panels accurately predicted two classes of outcomes. First, they identified several key inflection points along the disease trajectory, including onset of 1) amyloid plaque pathology (Aβ- to Aβ+;

SUBMITTER: Rathore S 

PROVIDER: S-EPMC12636643 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature