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A High Throughput, Multiplexed and Targeted Proteomic CSF Assay to Quantify Neurodegenerative Biomarkers and Apolipoprotein E Isoforms Status
Published on: October 20, 2016
Paired plasma lipidomics and proteomics analysis in the conversion from mild cognitive impairment to Alzheimer's
Alicia Gómez-Pascual1, Talel Naccache2, Jin Xu3
1Department of Information and Communications Engineering Faculty of Informatics, University of Murcia, Murcia, Spain; Steno Diabetes Center Copenhagen, Herlev, Denmark.
Background:
Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed.
Method:
Here, we propose a machine learning-based approach to detect the key metabolites and proteins involved in MCI progression to AD using data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study. Proteins and metabolites were evaluated separately in multiclass models (controls, MCI and AD) and together in MCI conversion models (MCI stable vs converter). Only features selected as relevant by 3/4 algorithms proposed were kept for downstream analysis.
Results:
Multiclass models of metabolites highlighted nine features further validated in an independent cohort (0.726 mean balanced accuracy). Among these features, one metabolite, oleamide, was selected by all the algorithms. Further in-vitro experiments in rodents showed that disease-associated microglia excreted oleamide in vesicles. Multiclass models of proteins stood out with nine features, validated in an independent cohort (0.720 mean balanced accuracy). However, none of the proteins was selected by all the algorithms. Besides, to distinguish between MCI stable and converters, 14 key features were selected (0.872 AUC), including tTau, alpha-synuclein (SNCA), junctophilin-3 (JPH3), properdin (CFP) and peptidase inhibitor 15 (PI15) among others.
Conclusions:
This omics integration approach highlighted a set of molecules associated with MCI conversion important in neuronal and glia inflammation pathways.
Insights
Machine learning identified key molecules, including the metabolite oleamide, involved in mild cognitive impairment (MCI) progression to Alzheimer's disease (AD). This approach aids in understanding neuroinflammation and developing diagnostic tools for AD.
Area of Science:
- Neuroscience
- Biochemistry
- Computational Biology
Background:
- Alzheimer's disease (AD) is a progressive neurodegenerative disorder with no cure.
- Mild cognitive impairment (MCI) is a precursor stage to AD, necessitating research into its progression mechanisms.
- Understanding the molecular changes from MCI to AD is crucial for early detection and intervention.
Purpose of the Study:
- To develop a machine learning model for identifying key metabolites and proteins associated with MCI progression to AD.
- To analyze multimodal biomarker data for distinguishing between control, MCI, and AD states, and between stable MCI and MCI converters.
- To uncover molecular pathways, particularly neuroinflammation, involved in MCI to AD transition.
Main Methods:
- Utilized machine learning algorithms to analyze proteomic and metabolomic data from the European Medical Information Framework for Alzheimer's Disease Multimodal Biomarker Discovery Study.
- Employed multiclass models to differentiate between controls, MCI, and AD, and binary models to predict MCI conversion.
- Selected features validated by at least three out of four algorithms and confirmed in an independent cohort.
Main Results:
- Metabolomic analysis identified nine key features, including oleamide, with high accuracy (0.726 mean balanced accuracy) in an independent cohort; oleamide was excreted by microglia.
- Proteomic analysis yielded nine features with significant predictive power (0.720 mean balanced accuracy), though no single protein was consistently selected by all algorithms.
- Models predicting MCI conversion identified 14 key features, including tTau, SNCA, JPH3, CFP, and PI15, with high performance (0.872 AUC).
Conclusions:
- An omics integration approach successfully identified molecular signatures associated with MCI progression to AD.
- The findings highlight the role of specific metabolites and proteins in neuronal and glial inflammation pathways.
- This research provides a foundation for developing novel biomarkers and therapeutic strategies for Alzheimer's disease.
