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Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
Published on: December 15, 2023
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Identifying Alzheimer's Disease Progression Subphenotypes via a Graph-based Framework using Electronic Health Records
Yu Huang1, Jie Xu2, Zhengkang Fan2
1Indiana University.
Research Square
|April 29, 2025
Summary
Alzheimer's disease (AD) progression is not uniform. This study identified four distinct AD progression subphenotypes in patients with mild cognitive impairment (MCI), revealing heterogeneous disease pathways for better understanding and treatment.
Area of Science:
- Neuroscience
- Medical Informatics
- Biostatistics
Background:
- Alzheimer's disease (AD) development exhibits significant heterogeneity, complicating diagnosis and treatment.
- Identifying distinct progression pathways is crucial for improving patient outcomes and developing targeted therapies.
Purpose of the Study:
- To identify disease progression subphenotypes in patients with mild cognitive impairment (MCI) and AD.
- To utilize electronic health records (EHRs) for uncovering novel AD progression pathways.
Main Methods:
- A cohort of 2,525 patients with MCI and AD was identified from the OneFlorida+ Clinical Research Consortium EHRs.
- An outcome-oriented graph neural network model was developed to analyze MCI to AD transition pathways.
Main Results:
- Four distinct Alzheimer's disease progression subphenotypes were identified.
- Progression times from MCI to AD varied significantly across subphenotypes, ranging from 805 to 1,236 days.
Conclusions:
- Alzheimer's disease progression is heterogeneous, not following uniform transitions.
- The proposed framework effectively identifies AD progression subphenotypes, offering explainable insights into disease development.
Keywords:
Alzheimer’s DiseaseDisease Progression SubphenotypingElectronic Health RecordsGraph Neural NetworkReal-world DataMore Related Videos
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