Abstract
Brain amyloid-β (Aβ) pathology is a core feature of Alzheimer disease (AD) and can be quantified using positron emission tomography (PET). Cerebrospinal fluid (CSF) and plasma biomarkers detect abnormal Aβ, but it is unclear to what degree they can predict quantitative Aβ-PET. We explored plasma and CSF biomarkers in relation to Aβ-PET in the BioFINDER-2 study (N = 1053), and the BioFINDER-1 study (N = 238). We developed a machine learning pipeline to predict Aβ-PET using CSF and plasma measures. The best models achieved R2 = 0.79. Plasma P-tau217 and CSF Aβ42/Aβ40 contributed the most. CSF Aβ42/Aβ40 contributed most to identify Aβ-positivity, while continuous Aβ-PET load within the positive range was best predicted by plasma P-tau217. Models using only plasma measures approached performance of CSF models. Altered metabolism of soluble Aβ may be highly associated with presence of Aβ plaques, while soluble P-tau217 levels may continue to change during build-up of Aβ pathology.
| Original language | English |
|---|---|
| Pages (from-to) | 217-231 |
| Number of pages | 15 |
| Journal | EMBO Molecular Medicine |
| Volume | 18 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 15 2026 |
Keywords
- Alzheimer’s Disease
- Amyloid
- Clinical Trials
- PET
- Plasma Biomarkers
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