Authors
Fan Zhang, Melissa Petersen, Mark Mapstone, James Hall, Benjamin Handen, Brad Christian, Elizabeth Head, Herminia Diana Rosas, Florence Lai, Joseph H Lee, Sharon J Krinsky-McHale, Frederick A Schmitt, Jordan Harp, Christy Hom, Ira T Lott, Sigan Hartley, Shahid Zaman, Beau M Ances, Lauren Ptomey, Jeffrey M Burns, Ann D Cohen, Adam M Brickman, Sid E O' Bryant, Alzheimer's Biomarkers Consortium‐Down Syndrome (ABC‐DS) Investigators*
Published in
Alzheimer's & dementia : the journal of the Alzheimer's Association. Volume 22. Issue 7. Pages e71659.
Abstract
Individuals with Down syndrome (DS) face high risk for Alzheimer's disease (AD), yet presymptomatic detection of cognitive decline is hindered by lifelong intellectual disability.
Using data from the Alzheimer's Biomarker Consortium-Down Syndrome (ABC-DS), blood samples from 246 participants were analyzed, yielding 404 longitudinal observations (45 Converters, 359 Stable) collected at 0, 16, and 32 months were analyzed. A Support Vector Machine was trained on 25 plasma biomarkers spanning neurodegeneration, inflammation, and vascular health, along with demographic factors (age, sex, ethnicity, karyotype, apolipoprotein E [APOE ε4]). Batch-effect correction and feature selection were applied, resulting in 13 key markers.
The refined model achieved 92.4% sensitivity, 59.9% specificity, and an area under the curve (AUC) of 77.9%, accurately identifying individuals at risk of cognitive decline up to 16 months before clinical progression.
This multi-domain, blood-based machine learning approach demonstrates that plasma biomarkers are valuable non-invasive tools for early detection and risk stratification of cognitive decline in DS.
PMID:
42449162
Bibliographic data and abstract were imported from PubMed on 15 Jul 2026.
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