A data-driven approach to complement the A/T/(N) classification system using CSF biomarkers.

ABSTRACT:

Aims

The AT(N) classification system not only improved the biological characterization of Alzheimer's disease (AD) but also raised challenges for its clinical application. Unbiased, data-driven techniques such as clustering may help optimize it, rendering informative categories on biomarkers' values.

Methods

We compared the diagnostic and prognostic abilities of CSF biomarkers clustering results against their AT(N) classification. We studied clinical (patients from our center) and research (Alzheimer's Disease Neuroimaging Initiative) cohorts. The studied CSF biomarkers included Aβ(1-42), Aβ(1-42)/Aβ(1-40) ratio, tTau, and pTau.

Results

The optimal solution yielded three clusters in both cohorts, significantly different in diagnosis, AT(N) classification, values distr

SUBMITTER: Hernandez-Lorenzo L 

PROVIDER: S-EPMC10848077 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature