Divergent amyloid trajectories distinguish normal aging from Alzheimer's disease progression

Alzheimer's disease (AD) is strongly associated with aging, but the precise point at which AD-related pathological progression diverges from normal aging is not well understood. This distinction becomes particularly challenging in the early stages of the disease, as populations defined clinically and biomarker-wise can include individuals exhibiting fundamentally different trajectories.

Researchers have now modeled AD progression as a deviation from aging using longitudinal amyloid PET imaging and a self-supervised trajectory-learning framework.

The analysis of the ADNI cohort revealed a single early path that splits into two separate branches - one associated with aging and the other linked to AD-related progression. These two branches exhibit distinct characteristics in terms of amyloid burden, cognitive decline, risk of progression to AD dementia, and genetic risk factors.

The researchers tested their model on a completely separate cohort from the NACC study, projecting unseen participants onto the trajectory without needing to retrain the model. This approach confirmed the bifurcating pattern, demonstrating the model's robustness and generalizability.

The bifurcating trajectory provides a biologically grounded framework for understanding early-stage heterogeneity in AD and enables risk stratification beyond the traditional binary amyloid status. In other words, this new approach can help identify individuals who are most at risk for developing AD, even before any clinical symptoms appear.

By providing a more nuanced understanding of how AD unfolds over time, this research offers hope for earlier detection and more targeted interventions to slow or prevent the disease's progression.