Abstract
INTRODUCTION
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.
METHODS
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.
RESULTS
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.
DISCUSSION
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.
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This post is Copyright: | July 14, 2026
Neuro-Dementia