Alzheimers Dement. 2025 Sep;21(9):e70702. doi: 10.1002/alz.70702.
ABSTRACT
INTRODUCTION: Cognitive decline in asymptomatic preclinical Alzheimer’s disease (AD) is slow and variable, limiting detection of treatment effects. This study developed models to forecast trajectories and improve trial efficiency.
METHODS: Models were trained on longitudinal Preclinical Alzheimer’s Cognitive Composite (PACC) data up to 240 weeks from the Phase III A4 study of solanezumab. Baseline inputs included demographics, apolipoprotein E (APOE) ε4, clinical scores, amyloid positron emission tomography (PET), plasma pTau217, magnetic resonance imaging (MRI), and tau PET (sub-study). Stochastic gradient boosting was used, with evaluation via cross-validation and trial simulations.
RESULTS: The best model without tau PET used pTau217, clinical, and MRI data (R2 = 0.32; area under the receiver operating characteristic curve (AUROC) for classifying a 0.5-point PACC decline = 78.6%). Replacing MRI with tau PET improved performance (R2 = 0.42; AUROC = 83.1%). Predicted trajectories as a prognostic covariate reduced sample sizes by 35% and increased power from 80% to 94.7%.
DISCUSSION: Prognostic models can predict decline in preclinical AD and improve trial efficiency.
GOV IDENTIFIERS: NCT02008357 (Clinical Trial of Solanezumab for Older Individuals Who May be at Risk for Memory Loss (A4)) HIGHLIGHTS: Models forecast 4.5-year cognitive decline in amyloid-positive preclinical Alzheimer’s disease (AD). Plasma pTau217 and tau positron emission tomography (PET) standardized uptake value ratios (SUVRs) in early-accumulating regions are key predictors. Tau PET improves prediction beyond plasma, magnetic resonance imaging (MRI), and clinical measures. Forecasted decline as a prognostic covariate improves power and cuts sample size in trial simulations. Alternative models underperform yet retain practical utility when tau PET or pTau217 is unavailable.
PMID:40990131 | DOI:10.1002/alz.70702
