Mapping the structure of depression biomarker research: A bibliometric analysis

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

Background

Vascular depression (VaD) is a depressive disorder closely associated with cerebrovascular disease and vascular risk factors. It remains underestimated owing to challenging diagnostics and limited information regarding the pathophysiological mechanisms of VaD. The purpose of this study was to analyze the proteomic signatures and identify the potential biomarkers with diagnostic significance in VaD.

Methods

Deep profiling of the serum proteome of 35 patients with VaD and 36 controls was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Functional enrichment analysis of the quantified proteins was based on Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and Reactome databases. Machine learning algorithms were used to screen candidate proteins and develop a protein-based model to effectively distinguish patients with VaD.

Results

There were 29 up-regulated and 31 down-regulated proteins in the VaD group compared to the controls (|log2FC| ≥ 0.26, p ≤ 0.05). Enrichment pathways analyses showed that neurobiological processes related to synaptic vesicle cycle and axon guidance may be dysregulated in VaD. Extrinsic component of synaptic vesicle membrane was the most enriched term in the cellular components (CC) terms. 19 candidate proteins were filtered for further modeling. A nomogram was developed with the combination of HECT domain E3 ubiquitin protein ligase 3 (HECTD3), Nidogen-2 (NID2), FTO alpha-ketoglutarate-dependent dioxygenase (FTO), Golgi membrane protein 1 (GOLM1), and N-acetylneuraminate lyase (NPL), which could be used to predict VaD risk with favorable efficacy.

Conclusion

This study offers a comprehensive and integrated view of serum proteomics and contributes to a valuable proteomics-based diagnostic model for VaD.

Oxidative Stress Biomarkers in Predictive Multi-Class Modeling of Depression Severity with Diabetes Mellitus, Cardiovascular Disease and Hypertension Comorbidity

In this study, depression severity was defined by the Patient Health Questionnaire (PHQ-9) and five machine learning algorithms were applied to classify depression severity in the presence of diabetes mellitus (DM), cardiovascular disease (CVD), and hypertension (HT) utilizing oxidative stress (OS) biomarkers (8-isoprostane, 8-hydroxydeoxyguanosine, reduced glutathione and oxidized glutathione), demographic details, and medication for eight hundred and thirty participants. The results show that the Random Forest (RF) outperformed other classifiers with the highest accuracy of 92% in a 4-class depression classification when considering all OS biomarkers along with DM, CVD and HT. RF also achieved the highest accuracy of 91% in 3-class classification when studying depression in presence of DM only and an accuracy of 88% and 87% in 5-class classification when investigating depression with CVD and HT, respectively. Moreover, RF performed best in the 3-class depression model with an accuracy of 85% when examining depression severity in the presence of OS biomarkers only. Our findings suggest that depression severity can be accurately identified with RF as a base classifier and that OS is a major contributor to depression severity in the presence of comorbidities. Biomarker analysis can supplement DSM-5-based diagnostics as part of personalized medicine and especially as point of care testing has become available for many of the given OS biomarkers.Clinical Relevance- Depression is the most common form of psychiatric disorder that has an oxidative stress etiology. Current diagnosis relies primarily on the Diagnostic and Statistical Manual for Mental Disorders (DSM-5), which may be too general and not informative for optimal multi-comorbidity diagnostics and treatment. Understanding the role of oxidative stress associated with depression can provide additional information for timely detection, comprehensive assessment, and appropriate intervention of depression illness.