Combinatorial panel with endophenotypes from multilevel information of diffusion tensor imaging and lipid profile as...

Area of Science:

• Neuroimaging
• Metabolomics
• Psychiatry

Background:

• Major depressive disorder (MDD) exhibits clinical heterogeneity, influenced by factors like genetic predisposition.
• Identifying distinct MDD subgroups using biomarkers is crucial for advancing precision medicine.

Purpose of the Study:

• To develop a diagnostic panel for genetically based MDD by integrating neuroimaging and metabolomic data.
• To enhance the identification of MDD endophenotypes for targeted therapeutic strategies.

Main Methods:

• Diffusion tensor imaging and plasma lipid profiling were conducted on 148 participants, including high-risk and first-episode MDD groups.
• Factor analysis and logistic regression were used to create a diagnostic panel integrating white matter integrity and lipid metabolite data.
• A fivefold cross-validation was employed to assess the panel's diagnostic performance.

Main Results:

• Shared alterations in white matter integrity (corona radiata, superior longitudinal fasciculus, corpus callosum) and unsaturated fatty acid metabolism were found in high-risk and first-episode MDD groups.
• The developed imaging x metabolite panel achieved an area under the curve of 0.86, outperforming individual measures.
• Cross-validation confirmed the panel's reliability and accuracy in diagnosing genetically influenced MDD.

Conclusions:

• Combining white matter integrity and unsaturated fatty acid profiles improves the identification of genetically based MDD endophenotypes.
• This integrated approach holds promise for advancing precision medicine in major depressive disorder.