Divergent amyloid trajectories distinguish normal aging from Alzheimer's disease progression
- 1Indiana University Indianapolis
Abstract
Aging is the greatest risk factor for Alzheimer's disease (AD), yet how and when AD-related pathological progression diverges from aging remains poorly understood. This distinction is particularly difficult at early stages, when clinically and biomarker-defined populations contain individuals following fundamentally different trajectories. Here, we model AD progression as a deviation from aging using longitudinal amyloid PET and a self-supervised trajectory-learning framework. In ADNI, the learned trajectory revealed a shared early path that bifurcated into an aging branch and an AD-related branch. The two branches showed distinct profiles in amyloid burden, cognitive decline, risk of progression to AD dementia, and genetic risk. Unseen participants from the independent NACC cohort were projected onto the trajectory without retraining, further reproducing key branch-specific biological and genetic patterns. Together, the bifurcating trajectory provides a biologically grounded framework for resolving early-stage heterogeneity and enabling risk stratification beyond binary amyloid status.
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BibTeX
@misc{tong2026divergent,
title = {{Divergent amyloid trajectories distinguish normal aging from Alzheimer's disease progression}},
author = {M. Tong and T. Gao and Y. Upadhyaya and K. Nho and S. Fang and A. Saykin and J. Yan and for the Alzheimer's Disease Neuroimaging Initiative},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.08.748727},
url = {https://www.biorxiv.org/content/10.64898/2026.09.08.748727v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Divergent amyloid trajectories distinguish normal aging from Alzheimer's disease progression
AU - M. Tong
AU - T. Gao
AU - Y. Upadhyaya
AU - K. Nho
AU - S. Fang
AU - A. Saykin
AU - J. Yan
AU - for the Alzheimer's Disease Neuroimaging Initiative
PY - 2026
DA - 2026/09/08
PB - bioRxiv
DO - 10.64898/2026.09.08.748727
UR - https://www.biorxiv.org/content/10.64898/2026.09.08.748727v1
AB - Aging is the greatest risk factor for Alzheimer's disease (AD), yet how and when AD-related pathological progression diverges from aging remains poorly understood. This distinction is particularly difficult at early stages, when clinically and biomarker-defined populations contain individuals following fundamentally different trajectories. Here, we model AD progression as a deviation from aging using longitudinal amyloid PET and a self-supervised trajectory-learning framework. In ADNI, the learned trajectory revealed a shared early path that bifurcated into an aging branch and an AD-related branch. The two branches showed distinct profiles in amyloid burden, cognitive decline, risk of progression to AD dementia, and genetic risk. Unseen participants from the independent NACC cohort were projected onto the trajectory without retraining, further reproducing key branch-specific biological and genetic patterns. Together, the bifurcating trajectory provides a biologically grounded framework for resolving early-stage heterogeneity and enabling risk stratification beyond binary amyloid status.
ER -