Dynamical A β -Tau-Neurodegeneration Model Predicts Alzheimer’s Disease Mechanisms and Biomarker Progression

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The study introduces a dynamical ATN (dATN) model to mechanistically simulate the spatiotemporal progression of amyloid-beta (Aβ), tau, and neurodegeneration biomarkers, extending the largely descriptive ATN framework. The authors integrate processes including prion-like protein aggregation of Aβ and tau, network-based tau propagation, Aβ-driven catalysis of tau progression, and tau-driven neurodegeneration, and they calibrate the model using multimodal longitudinal imaging from ADNI and BioFINDER-2, finding accurate fits to regional longitudinal biomarker data. Using the model, they report that Aβ-induced effects predict Braak-like cortical tau progression, that spatial colocalisation of Aβ and tau is crucial for biomarker acceleration, and that tau-driven atrophy correlates with observed neurodegeneration. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

ABSTRACT Alzheimer’s disease is characterised by the pathological interaction of two proteins, amyloid-beta (Aβ) and tau, which collectively drive neurodegeneration and cognitive decline. The progression of Aβ, tau, and neurodegeneration biomarkers is captured by the ATN framework, which is a powerful tool for disease classification. However, since the ATN framework is mainly descriptive, it cannot quantify or predict relationships between biomarkers over time. We address this limitation by introducing a dynamical ATN (dATN) model that mechanistically simulates the spatiotemporal progression of Aβ, tau, and neurodegeneration. The dATN model integrates mechanisms of prion-like protein aggregation of Aβ and tau, network-based tau propagation, Aβ-driven catalysis of tau progression, and tau-driven neurodegeneration. We calibrated the model using multimodal longitudinal imaging data from both the ADNI and BioFINDER-2 cohorts and show that it accurately fits longitudinal regional Aβ, tau, and neurodegeneration data. Using the dATN model, we show that Aβ-induced effects predict Braak-like cortical tau progression, that the spatial colocalisation of Aβ and tau is a crucial biomarker of disease acceleration, and that tau-driven atrophy strongly correlates with observed neurodegeneration. Furthermore, by integrating the disease progression model with pharmacokinetic–pharmacodynamic simulations, we present a powerful tool that facilitates regional evaluation of therapeutic strategies targeting Aβ, identification of critical intervention windows, and prediction of heterogeneous treatment effects across brain regions. This framework unifies mechanistic understanding with clinical imaging biomarkers, offering a quantitative approach for forecasting disease progression, testing mechanistic hypotheses, and optimising personalised treatment strategies in AD. One Sentence Summary Colocalisation of A β and tau predicts regional tau progression and optimal A β -targeted intervention windows, while tau predicts neurodegeneration.
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ABSTRACT Alzheimer’s disease is characterised by the pathological interaction of two proteins, amyloid-beta (Aβ) and tau, which collectively drive neurodegeneration and cognitive decline. The progression of Aβ, tau, and neurodegeneration biomarkers is captured by the ATN framework, which is a powerful tool for disease classification. However, since the ATN framework is mainly descriptive, it cannot quantify or predict relationships between biomarkers over time. We address this limitation by introducing a dynamical ATN (dATN) model that mechanistically simulates the spatiotemporal progression of Aβ, tau, and neurodegeneration. The dATN model integrates mechanisms of prion-like protein aggregation of Aβ and tau, network-based tau propagation, Aβ-driven catalysis of tau progression, and tau-driven neurodegeneration. We calibrated the model using multimodal longitudinal imaging data from both the ADNI and BioFINDER-2 cohorts and show that it accurately fits longitudinal regional Aβ, tau, and neurodegeneration data. Using the dATN model, we show that Aβ-induced effects predict Braak-like cortical tau progression, that the spatial colocalisation of Aβ and tau is a crucial biomarker of disease acceleration, and that tau-driven atrophy strongly correlates with observed neurodegeneration. Furthermore, by integrating the disease progression model with pharmacokinetic–pharmacodynamic simulations, we present a powerful tool that facilitates regional evaluation of therapeutic strategies targeting Aβ, identification of critical intervention windows, and prediction of heterogeneous treatment effects across brain regions. This framework unifies mechanistic understanding with clinical imaging biomarkers, offering a quantitative approach for forecasting disease progression, testing mechanistic hypotheses, and optimising personalised treatment strategies in AD. One Sentence Summary Colocalisation of Aβ and tau predicts regional tau progression and optimal Aβ-targeted intervention windows, while tau predicts neurodegeneration. Competing Interest Statement P.C. has received consultancy fees from Roche. NMC has received speaker/consultancy fees from Biogen, BioArctic, Eli Lilly, Merck, Novo Nordisk, and Owkin. OH is an employee of Lund University and Eli Lilly.

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