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Updated: Dec 9, 2025
Full- versus Sub-Regional Quantification of Amyloid-Beta Load on Mouse Brain Sections
Published on: May 19, 2022
Predicting Amyloid Burden to Accelerate Recruitment of Secondary Prevention Clinical Trials
O Langford1, R Raman, R A Sperling
1M.C. Donohue, Alzheimer's Therapeutic Research Institute, University of Southern California, San Diego, CA, USA, mdonohue@usc.edu.
Background:
Screening to identify individuals with elevated brain amyloid (Aβ+) for clinical trials in Preclinical Alzheimer's Disease (PAD), such as the Anti-Amyloid Treatment in Asymptomatic Alzheimer's disease (A4) trial, is slow and costly. The Trial-Ready Cohort in Preclinical/Prodromal Alzheimer's Disease (TRC-PAD) aims to accelerate and reduce costs of AD trial recruitment by maintaining a web-based registry of potential trial participants, and using predictive algorithms to assess their likelihood of suitability for PAD trials.
Objectives:
Here we describe how algorithms used to predict amyloid burden within TRC-PAD project were derived using screening data from the A4 trial.
Design:
We apply machine learning techniques to predict amyloid positivity. Demographic variables, APOE genotype, and measures of cognition and function are considered as predictors. Model data were derived from the A4 trial.
Setting:
TRC-PAD data are collected from web-based and in-person assessments and are used to predict the risk of elevated amyloid and assess eligibility for AD trials.
Participants:
Pre-randomization, cross-sectional data from the ongoing A4 trial are used to develop statistical models.
Measurements:
Models use a range of cognitive tests and subjective memory assessments, along with demographic variables. Amyloid positivity in A4 was confirmed using positron emission tomography (PET).
Results:
The A4 trial screened N=4,486 participants, of which N=1323 (29%) were classified as Aβ+ (SUVR ≥ 1.15). The Area under the Receiver Operating Characteristic curves for these models ranged from 0.60 (95% CI 0.56 to 0.64) for a web-based battery without APOE to 0.74 (95% CI 0.70 to 0.78) for an in-person battery. The number needed to screen to identify an Aβ+ individual is reduced from 3.39 in A4 to 2.62 in the remote setting without APOE, and 1.61 in the remote setting with APOE.
Conclusions:
Predictive algorithms in a web-based registry can improve the efficiency of screening in future secondary prevention trials. APOE status contributes most to predictive accuracy with cross-sectional data. Blood-based assays of amyloid will likely improve the prediction of amyloid PET positivity.
