Stratifying risk of Alzheimer's disease in healthy middle-aged individuals with machine learning.

ABSTRACT: Alzheimer's disease has a prolonged asymptomatic phase during which pathological changes accumulate before clinical symptoms emerge. This study aimed to stratify the risk of clinical disease to inform future disease-modifying treatments. Cerebrospinal fluid analysis from participants in the Emory Healthy Brain Study was used to classify individuals based on amyloid beta 42 (Aβ42), total tau (tTau) and phosphorylated tau (pTau) levels. Cognitively normal (CN), biomarker-positive (CN)/BM+individuals were identified using a tTau: Aβ42 ratio > 0.24, determined by Gaussian mixture models. CN/BM+ individuals (n = 134) were classified as having asymptomatic Alzheimer's disease (AsymAD), while CN, biomarker-negative (CN/BM-) individuals served as controls (n = 134). Cognitively sympt

SUBMITTER: Tandon R 

PROVIDER: S-EPMC11986205 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature