ArticleAlzheimer's & dementia : the journal of the Alzheimer's Association2020
Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning.
Article in Alzheimer's & dementia : the journal of the Alzheimer's Association, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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
37 citing papers in PubMed, 73 citations in OpenAlex.
- 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
- Neutrophil-mediated BDNF delivery for the treatment of moderate to severe Alzheimer's disease.Acta pharmaceutica Sinica. B · 2026Article
- Enhancing early Alzheimer's disease clinical trials through prognostic score covariate adjustment.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Predicting future dementia from routine clinical MRI and linked healthcare data.Alzheimer's research & therapy · 2026Article
- Multimodal MRI and machine learning for identifying depression in cerebral small vessel disease: a multicenter study.BMC medical imaging · 2026Article
- Predicting rates of cognitive and functional decline in Alzheimer's disease and mild cognitive impairment.Communications medicine · 2026Article
- Multimodal neuroimaging and AI integration in cognitive disorders: advances, challenges, and future directions for precision medicine.Psychoradiology · 2026Review
- Brain Atrophy Does Not Predict Clinical Progression in Progressive Supranuclear Palsy.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- Diagnostic and prognostic multimodal prediction models in Alzheimer's disease: A scoping review.Journal of Alzheimer's disease : JAD · 2025Article
- Predicted natural progression as an Alzheimer's prognostic covariate improves the precision of lecanemab efficacy assessments and clinical trial efficiency.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- The Potential of Disease Progression Modeling to Advance Clinical Development and Decision Making.Clinical pharmacology and therapeutics · 2025Review
- Deep multimodal learning for domain-level cognitive decline prediction in Alzheimer's disease.Frontiers in artificial intelligence · 2025Article
- Comparison of Ensemble Techniques for Early Prediction of Alzhiemer Disease.Research square · 2024Article
- Review
- Predicting clinical progression trajectories of early Alzheimer's disease patients.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Mining Alzheimer's disease clinical data: reducing effects of natural aging for predicting progression and identifying subtypes.Frontiers in neuroscience · 2024Article
- Consensus on rapid screening for prodromal Alzheimer's disease in China.General psychiatry · 2024Review
- Identifying major depressive disorder with associated sleep disturbances through fMRI regional homogeneity at rest.BMC psychiatry · 2023Article
- Algorithmic Fairness of Machine Learning Models for Alzheimer Disease Progression.JAMA network open · 2023Article
- Functional brain networks in the evaluation of patients with neurodegenerative disorders.Nature reviews. Neurology · 2023Review
Corrections and comments
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Authors and funding
26 authors at 10 institutions in 4 countries.
Funding
Abstract
introductionDeveloping cross-validated multi-biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge.
methodsWe applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural magnetic resonance imaging (MRI), amyloid-PET and fluorodeoxyglucose positron-emission tomography (FDG-PET) to predict rates of cognitive decline. Prediction models were trained in autosomal-dominant Alzheimer's disease (ADAD, n = 121) and subsequently cross-validated in sporadic prodromal AD (n = 216). The sample size needed to detect treatment effects when using model-based risk enrichment was estimated.
resultsA model combining all biomarker modalities and established in ADAD predicted the 4-year rate of decline in global cognition (R DISCUSSION: Our independently validated machine-learning model predicted cognitive decline in sporadic prodromal AD and may substantially reduce sample size needed in clinical trials in AD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.