ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2018
Characterizing heterogeneity in the progression of Alzheimer's disease using longitudinal clinical and neuroimaging biomarkers.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed.
- Multimodal prognostic modeling of individual cognitive trajectories to enhance trial efficiency in preclinical Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Trial
- Identifying sex-specific sub-phenotypes of Alzheimer's disease progression using longitudinal electronic health records.EBioMedicine · 2026Article
- Investigating a relation between amyloid beta plaque burden and accumulated neurotoxicity caused by amyloid beta oligomers.Medical & biological engineering & computing · 2026Article
- Identifying Alzheimer's Disease Progression Subphenotypes Via a Graph-based Framework Using Electronic Health Records.Journal of healthcare informatics research · 2026Article
- Identifying Sex-Specific Sub-phenotypes of Alzheimer's Disease Progression Using Longitudinal Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Deep normative modelling reveals insights into early-stage Alzheimer's disease using multi-modal neuroimaging data.Alzheimer's research & therapy · 2025Article
- The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making.Clinical pharmacology and therapeutics · 2025Review
- Pathway enrichment in genome-wide analysis of longitudinal Alzheimer's disease biomarker endophenotypes.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Predicting clinical progression trajectories of early Alzheimer's disease patients.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Early prediction of Alzheimer's disease and related dementias using real-world electronic health records.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Revealing Individual Neuroanatomical Heterogeneity in Alzheimer Disease Using Neuroanatomical Normative Modeling.Neurology · 2023Article
- Predicting the Risk of Alzheimer's Disease and Related Dementia in Patients with Mild Cognitive Impairment Using a Semi-Competing Risk Approach.Informatics (MDPI) · 2023Article
- Deep Learning for Brain MRI Confirms Patterned Pathological Progression in Alzheimer's Disease.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Article
- Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants.Journal of child psychology and psychiatry, and allied disciplines · 2022Article
- Using the Alzheimer's Disease Neuroimaging Initiative to improve early detection, diagnosis, and treatment of Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2022Review
- Neuroimaging Markers for Determining Former American Football Players at Risk for Alzheimer's Disease.Neurotrauma reports · 2022Article
- Differential Patterns of Domain-Specific Cognitive Complaints and Awareness Across the Alzheimer's Disease Spectrum.Frontiers in aging neuroscience · 2022Article
- Heterogeneity in Alzheimer's Disease Diagnosis and Progression Rates: Implications for Therapeutic Trials.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2022Review
- Beyond the average patient: how neuroimaging models can address heterogeneity in dementia.Brain : a journal of neurology · 2021Review
- Modeling Functional Limitations, Gait Impairments, and Muscle Pathology in Alzheimer's Disease: Studies in the 3xTg-AD Mice.Biomedicines · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
introductionModels characterizing intermediate disease stages of Alzheimer's disease (AD) are needed to inform clinical care and prognosis. Current models, however, use only a small subset of available biomarkers, capturing only coarse changes along the complete spectrum of disease progression. We propose the use of machine learning techniques and clinical, biochemical, and neuroimaging biomarkers to characterize progression to AD.
methodsWe used a large multimodal longitudinal data set of biomarkers and demographic and genotype information from 1624 participants from the Alzheimer's Disease Neuroimaging Initiative. Using hidden Markov models, we characterized intermediate disease stages. We validated inferred disease trajectories by comparing time to first clinical AD diagnosis. We trained an L2-regularized logistic regression model to predict disease trajectory and evaluated its discriminative performance on a test set.
resultsWe identified 12 distinct disease states. Progression to AD occurred most often through one of two possible paths through these states. Paths differed in terms of rate of disease progression (by 5.44 years on average), amyloid and total-tau (t-tau) burden (by 10% and 69%, respectively), and hippocampal neurodegeneration ( DISCUSSION: Progression to AD, in terms of biomarker trajectories, can be predicted based on participant-specific factors. Such disease staging tools could help in targeting high-risk patients for therapeutic intervention trials. As longitudinal data with richer features are collected, such models will help increase our understanding of the factors that drive the different trajectories of AD.
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