Serum proteomic biomarker investigation of vascular depression using data-independent acquisition: a pilot study

Area of Science:

• Neuroscience
• Biochemistry
• Genomics

Background:

• Vascular depression (VaD) is a complex depressive disorder linked to cerebrovascular disease and vascular risk factors.
• Current diagnostic challenges and limited understanding of VaD pathophysiology hinder effective management.
• Identifying reliable biomarkers is crucial for improving VaD diagnosis and treatment.

Purpose of the Study:

• To analyze serum proteomic signatures in patients with VaD.
• To identify potential protein biomarkers with diagnostic significance for VaD.
• To develop a predictive model for VaD risk using proteomic data.

Main Methods:

• Serum proteome profiling of 35 VaD patients and 36 controls using liquid chromatography-tandem mass spectrometry (LC-MS/MS).
• Functional enrichment analysis utilizing Gene Ontology (GO), KEGG pathway, and Reactome databases.
• Machine learning algorithms for candidate protein screening and development of a diagnostic model.

Main Results:

• Significant differences in protein expression were observed, with 29 proteins upregulated and 31 downregulated in VaD patients.
• Enrichment analyses indicated dysregulation in neurobiological processes, including synaptic vesicle cycle and axon guidance.
• A nomogram combining HECTD3, NID2, FTO, GOLM1, and NPL demonstrated favorable efficacy in predicting VaD risk.

Conclusions:

• This study provides a comprehensive proteomic profile of VaD.
• The developed proteomics-based diagnostic model offers a promising tool for VaD risk prediction.
• Further validation of these biomarkers could enhance clinical diagnostics for VaD.