ArticleScientific reports2017
Identification of clusters of rapid and slow decliners among subjects at risk for Alzheimer's disease.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers, 2 of them syntheses that pooled it.
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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
58 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Male- and female-specific reproductive risk factors across the lifespan for dementia or cognitive decline: a systematic review and meta-analysis.BMC medicine · 2023Pooled it
- The Application of Unsupervised Clustering Methods to Alzheimer's Disease.Frontiers in computational neuroscience · 2019Pooled it
- Causal effects of reproductive traits on cognitive function: A two-sample and multivariable mendelian randomization study.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Real-World Multimodal Machine Learning for Risk Enrichment Across the Alzheimer's Disease Spectrum.Journal of clinical medicine · 2026Article
- Women's midlife: the front line of Alzheimer prevention.The Journal of clinical investigation · 2026Review
- Consideration of sex as a biological variable over the history of the 5xFAD Alzheimer's Disease mouse model.Biology of sex differences · 2025Article
- Integrating imaging and omics for enhanced subtyping of mild cognitive impairment associated with Alzheimer's disease.Journal of translational medicine · 2025Article
- Tau PET positivity predicts clinically relevant cognitive decline driven by Alzheimer's disease compared to comorbid cases; proof of concept in the ADNI study.Molecular psychiatry · 2025Article
- Data-driven CSF biomarker profiling: imaging and clinical outcomes in a cohort at risk of Alzheimer's disease.Alzheimer's research & therapy · 2024Article
- AI for the prediction of early stages of Alzheimer's disease from neuroimaging biomarkers - A narrative review of a growing field.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2024Review
- From Mild Cognitive Impairment to Dementia: The Impact of Comorbid Conditions on Disease Conversion.Biomedicines · 2024Article
- Genetic-based patient stratification in Alzheimer's disease.Scientific reports · 2024Article
- Longitudinal regression of covariance matrix outcomes.Biostatistics (Oxford, England) · 2024Article
- Deep learning to predict rapid progression of Alzheimer's disease from pooled clinical trials: A retrospective study.PLOS digital health · 2024Article
- A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease.Biometrics · 2024Article
- Empirically derived psychosocial-behavioral phenotypes in Black/African American and Hispanic/Latino older adults enrolled in HABS-HD: Associations with AD biomarkers and cognitive outcomes.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Characterizing the clinical heterogeneity of early symptomatic Alzheimer's disease: a data-driven machine learning approach.Frontiers in aging neuroscience · 2024Article
- Personalized Computational Causal Modeling of the Alzheimer Disease Biomarker Cascade.The journal of prevention of Alzheimer's disease · 2024Article
- Identifying clinical phenotypes of frontotemporal dementia in post-9/11 era veterans using natural language processing.Frontiers in neurology · 2024Article
- Minding the Gap: Exploring Neuroinflammatory and Microglial Sex Differences in Alzheimer's Disease.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The heterogeneity of Alzheimer's disease contributes to the high failure rate of prior clinical trials. We analyzed 5-year longitudinal outcomes and biomarker data from 562 subjects with mild cognitive impairment (MCI) from two national studies (ADNI) using a novel multilayer clustering algorithm. The algorithm identified homogenous clusters of MCI subjects with markedly different prognostic cognitive trajectories. A cluster of 240 rapid decliners had 2-fold greater atrophy and progressed to dementia at almost 5 times the rate of a cluster of 184 slow decliners. A classifier for identifying rapid decliners in one study showed high sensitivity and specificity in the second study. Characterizing subgroups of at risk subjects, with diverse prognostic outcomes, may provide novel mechanistic insights and facilitate clinical trials of drugs to delay the onset of AD.
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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.