Graph Convolutional Network for AD and MCI Diagnosis Utilizing Peripheral DNA Methylation: Réseau de neurones en graphes pour le diagnostic de la MA et du TCL à l’aide de la méthylation de l’ADN périphérique

Nov 25, 2024Canadian journal of psychiatry. Revue canadienne de psychiatrie

Using Graph Neural Networks and Blood DNA Methylation to Diagnose Alzheimer's and Mild Cognitive Impairment

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Abstract

Blood DNA methylation levels differ remarkably across 17 identified sites in mild cognitive impairment and Alzheimer's disease.

Diagnostic models incorporating DNA methylation features showed improved performance in distinguishing between normal controls and those with cognitive impairment.
The area under the curve for normal control versus non-normal control classification reached 0.8534 when using DNA methylation in combination with cerebrospinal fluid features.
For mild cognitive impairment classification, the area under the curve was 0.7675 when integrating DNA methylation with other biomarkers.
A weak correlation (correlation coefficient ≤0.2) between DNA methylation and imaging or cerebrospinal fluid features in the normal control and mild cognitive impairment cohorts indicates potential for complementary use of these modalities.

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OBJECTIVE: Blood DNA methylation (DNAm) alterations have been widely reported in the onset and progression of mild cognitive impairment (MCI) and Alzheimer's disease (AD); however, DNAm is underutilized as a diagnostic biomarker for these diseases. We aimed to evaluate the diagnostic performance of DNAm for MCI and AD, both individually and in combination with well-established AD biosignatures.

METHODS: A total of 1,891 blood samples from Alzheimer's Disease Neuroimaging Initiative (ADNI) studies were used to identify potential candidate DNAm biomarkers. Multimodal clinical data from 635 samples (normal control (NC), = 193; MCI, = 352; AD, = 90) in the TADPOLE dataset were utilized to construct eight different classification models using a graph convolutional network, a machine learning framework. n n n

RESULTS: After feature selection, 17 DNAm sites were selected for subsequent analysis. Remarkable differences in DNAm levels were observed at the screened DNAm loci in all three cohorts. Adopting DNAm features into multimodal models significantly improved the classification performance for three dichotomous subtasks (NC vs. non-NC, MCI vs. non-MCI, and AD vs. non-AD), especially when combined with cerebrospinal fluid (CSF) features for NC (area under the curve (AUC): 0.8534) and MCI classification (AUC: 0.7675). A weak correlation between DNAm and both magnetic resonance imaging and CSF features in the NC and MCI cohorts suggests good complementarity between modalities (correlation coefficient ≤0.2).

CONCLUSIONS: Our study offers new insights into peripheral DNAm in MCI and AD and suggests promising diagnostic performance of models integrating epigenomics, imaging, or CSF biomarkers.

PLAIN LANGUAGE SUMMARY TITLE: Using Machine Learning and Blood-Based Genetic Markers to Help Diagnose Mild Cognitive Impairment and Alzheimer's Disease.

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Graph Convolutional Network for AD and MCI Diagnosis Utilizing Peripheral DNA Methylation: Réseau de neurones en graphes pour le diagnostic de la MA et du TCL à l’aide de la méthylation de l’ADN périphérique

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