# Amyloid Probability in Alzheimer Disease From Plasma, Cerebrospinal Fluid, and Amyloid Imaging

## TL;DR

This study evaluates how blood, spinal fluid, and brain imaging can be combined to better estimate amyloid presence in Alzheimer's disease.

## Contribution

The study introduces Bayesian latent class models to estimate amyloid positivity probabilities using multiple biomarkers.

## Key findings

- Combining PET, CSF, and plasma biomarkers improves amyloid probability estimation compared to single methods.
- Discordance between PET and CSF results occurs even when samples are collected within 7 days.
- Plasma biomarkers refine probabilities in discordant cases, especially under conditional dependence assumptions.

## Abstract

Blood-based biomarkers are increasingly used to triage patients for amyloid confirmation, yet performance is often reported versus a single comparator despite discordance between amyloid PET and CSF. In a cross-sectional secondary analysis of the Alzheimer’s Disease Neuroimaging Initiative (ADNI), we assembled one PET-anchored PET–CSF–plasma triad per participant (n = 320). Bayesian latent class models integrating PET, CSF and plasma (Aβ42/40 or %p-tau217) estimated pattern-level posterior probabilities of latent amyloid positivity, with prespecified sensitivity analyses for PET–CSF conditional dependence, timing gaps (≤ 7, 8–30, > 30 days) and CSF cutpoints. Concordant PET+/CSF + and PET−/CSF − patterns mapped to probabilities near 1 and 0, whereas discordant patterns yielded intermediate probabilities refined by plasma strata and most sensitive to dependence assumptions. PET–CSF discordance occurred even within ≤ 7 days (12/98; 12%). A CSF Aβ42 coverage analysis showed similar gradients. Pattern-to-probability reporting may aid interpretation without privileging a reference test.