ArticleAlzheimer's research & therapy2022
Model-based stratification of progression along the Alzheimer disease continuum highlights the centrality of biomarker synergies.
Article in Alzheimer's research & therapy, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Stratification of Alzheimer's disease patients using knowledge-guided unsupervised latent factor clustering with electronic health record data.Communications medicine · 2026Article
- Cohort-specific boolean models highlight different regulatory modules during Parkinson's disease progression.iScience · 2024Article
- Genetic-based patient stratification in Alzheimer's disease.Scientific reports · 2024Article
- Deep learning to predict rapid progression of Alzheimer's disease from pooled clinical trials: A retrospective study.PLOS digital health · 2024Article
- Detection of Alzheimer's disease onset using MRI and PET neuroimaging: longitudinal data analysis and machine learning.Neural regeneration research · 2023Review
- Identifying the regional substrates predictive of Alzheimer's disease progression through a convolutional neural network model and occlusion.Human brain mapping · 2022Article
- Potential Applications of Artificial Intelligence in Clinical Trials for Alzheimer's Disease.Life (Basel, Switzerland) · 2022Review
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4 authors.
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Abstract
backgroundThe progression rates of Alzheimer's disease (AD) are variable and dynamic, yet the mechanisms that contribute to heterogeneity in progression rates remain ill-understood. Particularly, the role of synergies in pathological processes reflected by biomarkers for amyloid-beta ('A'), tau ('T'), and neurodegeneration ('N') in progression along the AD continuum is not fully understood.
methodsHere, we used a combination of model and data-driven approaches to address this question. Working with a large dataset (N = 321 across the training and testing cohorts), we first applied unsupervised clustering on longitudinal cognitive assessments to divide individuals on the AD continuum into those showing fast vs. moderate decline. Next, we developed a deep learning model that differentiated fast vs. moderate decline using baseline AT(N) biomarkers.
resultsTraining the model with AT(N) biomarker combination revealed more prognostic utility than any individual biomarkers alone. We additionally found little overlap between the model-driven progression phenotypes and established atrophy-based AD subtypes. Our model showed that the combination of all AT(N) biomarkers had the most prognostic utility in predicting progression along the AD continuum. A comprehensive AT(N) model showed better predictive performance than biomarker pairs (A(N) and T(N)) and individual biomarkers (A, T, or N).
conclusionsThis study combined data and model-driven methods to uncover the role of AT(N) biomarker synergies in the progression of cognitive decline along the AD continuum. The results suggest a synergistic relationship between AT(N) biomarkers in determining this progression, extending previous evidence of A-T synergistic mechanisms.
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