Abstract
Objective
To evaluate the predictive utility of baseline plasma biomarkers and neuropsychological measures in identifying cognitively unimpaired older adults at risk of cognitive and functional decline over five years.
Background
The clinical and biological heterogeneity observed in Alzheimer’s disease (AD) complicates design of trials and the identification of appropriate participants. Identifying practical tools to define more homogenous subgroups could enhance clinical trial enrichment and improve early detection.
Methods
We analyzed data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer’s Disease (A4) trial and its companion Evaluation of Amyloid Risk and Neurodegeneration (LEARN) observational study. The sample included 866 cognitively unimpaired, amyloid-positive individuals from the A4 trial (comprising participants randomized to receive Solanezumab or placebo) and 343 cognitively unimpaired, amyloid-negative individuals from LEARN. Cognitive/functional decline was defined as an increase of ≥0.5 in Clinical Dementia Rating–Global Score (CDR-GS) during a 240-week period. Using multiple logistic regression models, we evaluated the predictive value of demographic variables, APOE4 status, amyloid PET SUVR, plasma P-tau217, and Alzheimer’s Disease Cooperative Study–Preclinical Alzheimer’s Cognitive Composite (ADCS-PACC) in three groups: A4-Solanezumab, A4-placebo and LEARN. In a sub-study including 656 participants with available data, we assessed the incremental value of additional plasma biomarkers (Aβ42/Aβ40 ratio, GFAP, and NfL).
Results
Both plasma P-tau217 and ADCS-PACC significantly improved predictive performance over a base model with demographics and APOE4. The full model combining all predictor variables yielded the highest AUCs across A4 Solanezumab (0.80 ± 0.06), A4 Placebo (0.80 ± 0.06), and LEARN (0.78 ± 0.08). Adding other plasma biomarkers yielded small but consistent improvements in AUC (1–3%).
Conclusions
Plasma P-tau217 and ADCS-PACC, individually and in combination, improved prediction of cognitive/functional decline in asymptomatic older adults. Predictive models incorporating these scalable and non-invasive measures improved clinical trial enrichment and earlier identification of at-risk individuals preclinical AD.
Full Text Availability
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