Genetic-based patient stratification in Alzheimer's disease
Laura Hernández-Lorenzo1, Fernando García-Gutiérrez2, Ana Solbas-Casajús2
1Department of Computer Architecture and Automation, Computer Science Faculty, Complutense University of Madrid, 28040, Madrid, Spain. laurahl@ucm.es.
Scientific Reports
|May 1, 2024
Summary
Alzheimer's disease patients can be grouped by genetic risk factors. This stratification reveals distinct genetic profiles, even with similar symptoms, highlighting key genes for personalized treatments.
Area of Science:
- Neuroscience
- Genetics
- Computational Biology
Background:
- Alzheimer's disease (AD) exhibits significant heterogeneity in pathology and symptoms.
- Genetics represents a major risk factor influencing AD development and progression.
Purpose of the Study:
- To develop a patient stratification technique for Alzheimer's disease based on genetic risk factors.
- To investigate the biological mechanisms underlying AD heterogeneity using network biology and clustering.
Main Methods:
- Mapped genetic variants to a brain-specific protein-protein interaction (PPI) network.
- Generated individualized PPI scores for clustering analysis.
- Phenotyped identified clusters using genetic, demographic, biomarker, imaging (FDG-PET), and neurocognitive data.
Main Results:
- Identified three distinct patient clusters primarily defined by genetic variants in MAPT, APP, and APOE.
- Observed minimal variation in clinical symptoms and pathology across clusters despite genetic differences.
- Highlighted MAPT, APP, and APOE as key genes associated with AD genetic architectures.
Conclusions:
- Different biological pathways can lead to similar Alzheimer's disease presentations.
- Genetic stratification based on network biology offers insights into AD heterogeneity.
- MAPT, APP, and APOE are potential targets for developing personalized therapeutic strategies for AD subgroups.
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