ArticleNeurology2025
Integrating MRI Volume and Plasma p-Tau217 for Amyloid Risk Stratification in Early-Stage Alzheimer Disease.
Article in Neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Blood-Based Tau as a Biomarker for Early Detection and Monitoring of Alzheimer's Disease: A Systematic Review and Meta-Analysis.International journal of molecular sciences · 2025Pooled it
- Exploring longitudinal relationships among Alzheimer's disease biomarkers.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2026Article
- Olfactory radiomics signatures link pTau217 to cognitive impairment in probable Alzheimer's disease: a multi-cohort machine learning study.Alzheimer's research & therapy · 2026Article
- CT-derived brain volumes and plasma p-Tau217 for risk stratification of amyloid positivity in early-stage Alzheimer's disease.Alzheimer's research & therapy · 2025Article
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22 authors.
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Abstract
BACKGROUND AND
objectivesIdentifying β-amyloid (Aβ) positivity is crucial for selecting candidates for Aβ-targeted therapies in early-stage Alzheimer disease (AD). While Aβ PET is accurate, its high cost limits routine use. Plasma p-tau217 testing offers a less invasive option but also incurs additional costs. Structural brain MRI, routinely used in cognitive assessments, can identify features predictive of Aβ positivity without extra expense. We evaluated a 2-stage workflow integrating MRI-based features and plasma p-tau217 to efficiently predict Aβ PET positivity in early-stage AD.
methodsThis prospective cohort study included participants with mild cognitive impairment (MCI) or early Alzheimer-type dementia (ATD) from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD; Korea) and Alzheimer's Disease Neuroimaging Initiative (ADNI; US) cohorts. Eligible participants had a Clinical Dementia Rating score of 0.5, along with MRI, plasma p-tau217, and Aβ PET data. A random forest classifier predicting Aβ PET positivity was developed using MRI-based brain atrophy patterns and APOE ε4 status. Participants were stratified into low-risk, intermediate-risk, and high-risk groups; plasma p-tau217 testing was performed only in intermediate-risk individuals. Outcomes included positive predictive value (PPV), negative predictive value (NPV), and overall accuracy.
resultsA total of 807 K-ROAD participants (median age 72.0 years, 58.7% female) and 230 ADNI participants (median age 70.9 years, 49.1% female) were analyzed. Using a 95% sensitivity/specificity strategy, the low-risk group demonstrated NPVs of 94.7% (91.7%-97.7%, K-ROAD) and 99.0% (97.0%-100.0%, ADNI). The high-risk group showed PPVs of 97.6% (95.9%-99.3%, K-ROAD) and 98.8% (96.5%-100.0%, ADNI). Intermediate-risk groups comprised 33.3% (K-ROAD) and 20.9% (ADNI) of participants. Plasma p-tau217 testing in intermediate-risk groups yielded PPVs of 92.5% (88.7%-96.3%, K-ROAD) and 90.0% (79.0%-100.0%, ADNI) and NPVs of 83.1% (75.0%-91.2%, K-ROAD) and 83.3% (66.1%-100.0%, ADNI). The overall workflow accuracy was 94.2% (92.6%-95.8%, K-ROAD) and 96.5% (94.1%-98.9%, ADNI). DISCUSSION: The 2-stage diagnostic workflow integrating MRI-based risk stratification and plasma p-tau217 testing accurately identified individuals with Aβ PET positivity in early-stage AD, substantially reducing the need for additional biomarker testing. However, the generalizability may be limited by modest incremental improvement over baseline models and limited racial and ethnic diversity.
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