DEVELOPMENT AND APPLICATION OF BRAIN TISSUE BASED MULTI-OMICS PROFILE SCORES FOR ALZHEIMER'S DISEASE
Timur Tug1,2, Donghai Liang2,3, Sven Teschke1
1Department of Statistics, TU Dortmund University, Dortmund, Germany.
Research Square
|May 7, 2026
Summary
This study integrated DNA methylation and metabolomics to understand Alzheimer's disease (AD) pathology. Both omics layers converged on lipid metabolism and signal transduction, revealing shared biological mechanisms in AD.
Area of Science:
- Neuroscience
- Genomics
- Metabolomics
Background:
- Omics technologies like epigenomics and metabolomics offer insights into Alzheimer's disease (AD) mechanisms.
- The interaction between different omics layers and AD neuropathology remains poorly understood.
Purpose of the Study:
- To comprehensively analyze single- and multi-omics data for Alzheimer's disease (AD) pathology.
- To investigate the interaction of DNA methylation and metabolomics in relation to AD neuropathology.
Main Methods:
- Integrated genome-wide DNA methylation and high-resolution metabolomics data from 157 frontal cortex samples.
- Developed novel single- and multi-omics profile scores (PS) for AD pathology using machine learning, regression, and pathway analysis.
Main Results:
- DNA methylation profile scores outperformed metabolomics scores for the ABC score (median R²: 0.11 vs. 0.04).
- A multi-omics PS combining both layers yielded a partial R² of 0.15 for the ABC score, independent of covariates.
- Both omics layers converged on lipid metabolism and signal transduction pathways implicated in AD.
Conclusions:
- Integrative pathway and network analyses of DNA methylation and metabolomics PS highlight shared biological mechanisms in AD.
- Multi-omics approaches provide valuable biological insights into Alzheimer's disease, even with limited predictive accuracy gains.
Keywords:
Alzheimer’s diseaseDNA methylationmachine learningmetabolomicsmulti-omicsneuropathologyprofile scoresMore Related Videos
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