Metabolic Biomarkers, White Matter Microstructure and Functional Connectivity in 5-Year-Old Children
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Background: Human brain development is characterized by progressive maturation and myelination of white matter tracts that are essential for cognitive and emotional functioning through efficient neural communication. Although this development is shaped by metabolic pathways that regulate the brain’s biochemical environment, no study has examined how the metabolome may be associated with white matter or functional brain outcomes in young children.
Objective: To explore the relationship between the serum metabolome and white matter microstructure and functional connectivity in typically developing 5-year-old children from the Finn-Brain Birth Cohort Study.
Methods: Diffusion-weighted imaging data from children (n=140) were acquired with 3T scanner. Diffusion tensor imaging and tract-based spatial statistics were used to derive fractional anisotropy (FA) and mean diffusivity (MD) maps, whereas tracts were identified through probabilistic tractography. FA, MD, and tract probability maps were integrated using FSL Independent Component Analysis (FLICA) to generate 10 multimodal white matter components, which represent the main patterns of structural covariance across the whole white matter. Functional connectivity was assessed by functional connectome harmonics (FCH) derived from resting-state blood-oxygen level-dependent (BOLD) signals and entropy. Serum metabolomics/exposomics was performed using ultra-high- performance liquid chromatography quadrupole time-of-flight mass spectrometry (UHPLC- QTOFMS). The serum metabolome was studied both as individual metabolites and as metabolite clusters derived through Gaussian mixture modelling. Candidate biomarkers were selected based on partial correlation and elastic net regression. All models were adjusted for child sex, age, and BMI. The data was analyzed using R statistical software.
Results: Ten multimodal white matter components and functional connectome harmonics were assessed for association with serum metabolome. Metabolite cluster 6, primarily composed of fatty acids, demonstrated an FDR-significant correlation with white matter component IC6 which was dominated by whole white matter MD. None of the associations survived correction for multiple comparisons with FCH. At the single metabolite level, partial correlation analysis identified a strong negative association between the lysophosphatidylcholine LPC_22_6 and white component IC6. Voxel-wise modelling identified a large extended set of voxels (~40,050 voxels) in which this metabolite was associated with reduced MD (TFCE-corrected p<0.05).
Conclusion: Overall, white matter components showed a stronger association with the serum metabolome than functional connectome harmonics. Specifically, circulating lipids and lysophosphatidylcholines are associated with white matter microstructure throughout the brain, providing novel peripheral biomarkers of white matter development in young children.