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Digital cognition plus plasma p‐Tau217 and Aβ42/40 powerfully predict Alzheimer's progression

Abstract:
INTRODUCTION: Alzheimer's disease is characterized by amyloid‐β (Aβ) and tau accumulation. Identifying individuals with rapid proteinopathy progression is crucial for timely intervention and trial enrichment. METHODS: We analyzed longitudinal data from 456 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants, including 375 with digital cognitive test scores. Amyloid and tau accumulation rates were estimated from positron emission tomography (PET) imaging using linear mixed‐effects models. Participants were classified as fast or slow accumulators via Gaussian modeling. Predictors of accumulation and clinical conversion were assessed with logistic and Cox regression models, incorporating demographics, cognitive measures and plasma biomarkers. RESULTS: Plasma p‐tau217 and Aβ42/Aβ40 predicted rapid accumulation and conversion, with p‐tau217 the strongest marker (odds ratio [OR] up to 6.6). Baseline digital cognitive measures contributed significantly to the prediction, achieving comparable or superior predictive accuracy to traditional cognitive tests (area under the curve [AUC] up to 0.92; C‐index 0.82). DISCUSSION: Plasma p‐tau217 and Aβ42/Aβ40 emerged as robust predictors of the progression of disease pathology, supported by cognitive measures.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/dad2.70281

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Institution:
University of Oxford
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Author
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Institution:
University of Oxford
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Author
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Institution:
University of Oxford
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Author


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Funder identifier:
https://ror.org/029chgv08
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Funder identifier:
https://ror.org/05p21nq18
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Funder identifier:
https://ror.org/0472gwq90
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Funder identifier:
https://ror.org/03x94j517
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Funder identifier:
https://ror.org/049v75w11


Publisher:
Wiley
Journal:
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring More from this journal
Volume:
18
Issue:
1
Article number:
e70281
Publication date:
2026-02-28
Acceptance date:
2026-01-19
DOI:
EISSN:
2352-8729
ISSN:
2352-8729


Language:
English
Keywords:
Pubs id:
2384430
Local pid:
pubs:2384430
Source identifiers:
3810163
Deposit date:
2026-02-28
ARK identifier:
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