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Updated: Jan 7, 2026
07:20
Dried Blood Spot Collection of Health Biomarkers to Maximize Participation in Population Studies
Published on: January 28, 2014
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Biomarkers
Anna-Katharine Brem1,2, Zunera Khan3, Ellie Pickering4
1University Hospital of Old Age Psychiatry, University of Bern, Bern, Switzerland.
Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025
Summary
The PREDICTOM study uses artificial intelligence (AI) to screen for Alzheimer's disease (AD) risk using home-based biomarkers. Early results show potential for differentiating high-risk individuals for timely intervention.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Biomarker Discovery
Background:
- Early detection of Alzheimer's disease (AD) is a critical unmet medical need.
- Existing screening methods often lack accessibility and scalability for widespread early detection.
Purpose of the Study:
- To develop and validate an AI-driven screening platform for predicting and detecting AD.
- To extend AD screening to a home-based setting using novel and established biomarkers.
Main Methods:
- The PREDICTOM study is a pan-European cohort of 4000 at-risk individuals over 50.
- Level 1 involves home-based digital and physiological biomarker assessments.
- AI algorithms will stratify participants into high and low AD risk groups for further in-clinic assessment (Level 2) and confirmation (Level 3).
Main Results:
- An interim analysis will include data from approximately 1000 participants across the AD risk spectrum.
- Initial findings will focus on the accuracy of at-home measures in differentiating risk stages.
Conclusions:
- The PREDICTOM study aims to provide significant insights into the diagnostic accuracy of at-home AD screening tools.
- The platform has the potential to revolutionize early AD detection and management through accessible, AI-powered risk stratification.
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