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Related Experiment Video

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Navigating the Mass Spectrometry-Based Proteomic Data Using Free Computational Tools
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Dissection of depression heterogeneity using proteomic clusters.

Marije van Haeringen1, Yuri Milaneschi1, Femke Lamers1

  • 1Department of Psychiatry, Amsterdam UMC, Vrije Universiteit, Amsterdam Public Health Research Institute and Amsterdam Neuroscience, Amsterdam, The Netherlands.

Psychological Medicine
|January 18, 2022
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Summary

Major depressive disorder (MDD) biomarkers are complex. A network of inflammatory and metabolic proteins is linked to MDD, particularly symptoms related to energy imbalance like hypersomnia and appetite changes.

Keywords:
DepressionWGCNAheterogeneityproteomics

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Area of Science:

  • Biochemistry
  • Psychiatry
  • Network Biology

Background:

  • Major depressive disorder (MDD) is heterogeneous, making biomarker discovery challenging.
  • Limited biomarker research may not capture complex network-level alterations.
  • This study investigated protein clusters and their association with MDD and specific symptoms.

Purpose of the Study:

  • To identify protein clusters associated with MDD.
  • To explore the relationship between these protein clusters and individual depressive symptoms.
  • To understand the biological pathways involved in MDD heterogeneity.

Main Methods:

  • Utilized data from 1621 subjects in the Netherlands Study of Depression and Anxiety (NESDA).
  • Assessed MDD using DSM-IV criteria and measured 30 depressive symptoms with the Inventory of Depressive Symptomatology.
  • Analyzed serum protein levels (171 analytes) and computed proteomic clusters using weighted gene co-expression network analysis (WGCNA).

Main Results:

  • Identified six proteomic clusters, with one significantly associated with current MDD (p=0.0096).
  • This cluster, containing 21 analytes, was enriched in inflammation and metabolism pathways (e.g., CRP, leptin, insulin).
  • The cluster correlated with ten symptoms, notably atypical, energy-related ones like hypersomnia, increased appetite, and weight gain, even after covariate adjustment.

Conclusions:

  • Protein network alterations in inflammation and metabolism are implicated in MDD.
  • These alterations are primarily linked to clinical symptoms reflecting energy intake/expenditure imbalance.
  • Findings highlight the role of interconnected biological processes in MDD symptomatology.