Abstract
INTRODUCTION: Blood-based biomarkers, most notably plasma phosphorylated tau (p-tau)217, have transformed the diagnostic landscape of Alzheimer's disease (AD). METHODS: We applied an unsupervised machine learning approach to tau positron emission tomography (PET) imaging in 606 participants (age 73.95 ± 7.72; 309 female) to identify AD subtypes. Within each subtype, we evaluated plasma p-tau217 levels, their association with regional tau PET uptake, differences between cognitively unimpaired (CU) and cognitively impaired (CI) individuals, and relationships to cognitive performance. RESULTS: Four subtypes were identified: limbic, medial temporal lobe (MTL) sparing, posterior, and lateral temporal (l temporal). Plasma p-tau217 was elevated in CI versus CU in limbic, posterior, and l temporal subtypes and strongly associated with tau deposition and cognitive performance. In the MTL-sparing subtype, p-tau217 showed a significant association with tau but no elevation in CI and no relationship to cognition. DISCUSSION: These findings indicate that p-tau217's diagnostic utility varies across AD subtypes, reflecting distinct biological mechanisms not captured by current blood biomarkers. HIGHLIGHTS: Plasma phosphorylated tau (p-tau)217 differentiated cognitively unimpaired from impaired individuals in most subtypes, with the notable limitation of the medial temporal lobe (MTL)-sparing group.P-tau217 level was linked to regional tau accumulation as measured by tau positron emission tomography, across all subtypes.The MTL-sparing subtype appeared to be unique, as p-tau217 was not elevated in cognitively impaired individuals, and there was no clear relationship between p-tau217 levels and cognitive performance.