ArticleNature communications2025
AI-guided patient stratification improves outcomes and efficiency in the AMARANTH Alzheimer's Disease clinical trial.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed.
- The Substrate-Engine Model of Ferroptosis: Stage-Specific Targeting of Acyl-CoA Synthetase Long-Chain Family Member 4 and 5‑Lipoxygenase in Alzheimer's Disease.ACS pharmacology & translational science · 2026Review
- Turning failure into success: how artificial intelligence can help personalize therapies and re-use patient data.Purinergic signalling · 2026Article
- Next generation preventive neurology: how artificial intelligence and machine learning are reshaping Alzheimer's disease research.Behavioral and brain functions : BBF · 2026Review
- Artificial intelligence and machine learning for precision prevention of cognitive decline: integrating multimodal biomarkers, lifestyle interventions, and natural medicines from prediction to clinical practice.Frontiers in aging neuroscience · 2026Review
- AI-augmented frameworks for enhancing Alzheimer's disease clinical trials: A memory clinic perspective.The journal of prevention of Alzheimer's disease · 2025Article
- Solving the 'Goldilocks problem' in dementia clinical trials with multimodal AI.The journal of prevention of Alzheimer's disease · 2025Article
- Redox-Active Polyphenol Red Molecularly Imprinted Polymers on Porous Gold Electrodes for Ultrasensitive, AI-Assisted Detection of Alzheimer's Biomarkers in Undiluted Biofluids.Advanced healthcare materials · 2025Article
- Collaborative AI for precision neurorehabilitation: a roadmap.Journal of neuroengineering and rehabilitation · 2025Review
- Application and challenges of DeepSeek in primary care in China.Frontiers in public health · 2025Review
- Recent Advances in the Application of Artificial Intelligence in Alzheimer's Disease.Current Alzheimer research · 2025Review
- Brain-Targeting siRNA Delivery to Tackle Alzheimer's Disease.Wiley interdisciplinary reviews. Nanomedicine and nanobiotechnologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Alzheimer's Disease (AD) drug discovery has been hampered by patient heterogeneity, and the lack of sensitive tools for precise stratification. Here, we demonstrate that our robust and interpretable AI-guided tool (predictive prognostic model, PPM) enhances precision in patient stratification, improving outcomes and decreasing sample size for a AD clinical trial. The AMARANTH trial of lanabecestat, a BACE1 inhibitor, was deemed futile, as treatment did not change cognitive outcomes, despite reducing β-amyloid. Employing the PPM, we re-stratify patients precisely using baseline data and demonstrate significant treatment effects; that is, 46% slowing of cognitive decline for slow progressive patients at earlier stages of neurodegeneration. In contrast, rapid progressive patients did not show significant change in cognitive outcomes. Our results provide evidence for AI-guided patient stratification that is more precise than standard patient selection approaches (e.g. β-amyloid positivity) and has strong potential to enhance efficiency and efficacy of future AD trials.
Indexed as
Identifiers
What OpenQuestion holds
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.