Korean Study Explores Whether Blood RNA Profiles Could Help Classify Alzheimer's Disease

SEOUL, South Korea - June 18, 2025 - PRLog -- A  recently  published study from  Seoul  National  University  Bundang  Hospital (SNUBH) and Indiana University explores whether peripheral blood transcriptomic data could contribute to non-invasive methods of classifying Alzheimer's disease.

Published in Alzheimer's & Dementia, the study analyzed RNA-sequencing data from two independent Korean cohorts—248 in a discovery cohort and 275 in validation—to investigate whether gene expression patterns in blood might help distinguish early- onset (EOAD) and late-onset Alzheimer's disease (LOAD) from cognitively normal participants.

The researchers identified 18 differentially expressed genes (DEGs) in EOAD and 88 in LOAD. Network analysis identified LOAD-associated pathways such as mitophagy, AMPK signaling, and ubiquitin-mediated proteolysis. Two genes—SMOX and PLVAP—were found to correlate inversely with cortical amyloid-β burden in LOAD as  measured  by  PET  imaging.

A  machine  learning  model  trained  on  the  most discriminative DEGs showed promise in classifying LOAD versus normal cognition, achieving a mean AUC of 0.932.

"This is an exploratory study," said senior author Dr. Young Ho Park. "Our goal was not to identify diagnostic markers, but to investigate whether transcriptomic data from blood might support alternative approaches to Alzheimer's classification, especially in settings where traditional tests are less accessible."

PET imaging is expensive, and CSF analysis via lumbar puncture is more invasive than a routine blood test, making both methods less practical for routine screening. While this research is still in early stages, it adds to a broader body of studies exploring whether blood-based data could help expand options for assessment.

This is one of the few large-scale transcriptomic comparisons of EOAD and LOAD in East Asian populations, and is the first in a Korean cohort. The authors note that further research,  including  validation  in  diverse  cohorts,  will  be  critical  for  assessing generalizability and clinical relevance.

https://www.snubh.org/dh/en/