Dissection of depression heterogeneity using proteomic clusters.

Marije Van Haeringen,Rick Jansen,Brenda W J H Penninx,Femke Lamers,Yuri Milaneschi

doi:10.1017/s0033291721004888

Abstract

The search for relevant biomarkers of major depressive disorder (MDD) is challenged by heterogeneity; biological alterations may vary in patients expressing different symptom profiles. Moreover, most research considers a limited number of biomarkers, which may not be adequate for tagging complex network-level mechanisms. Here we studied clusters of proteins and examined their relation with MDD and individual depressive symptoms. The sample consisted of 1621 subjects from the Netherlands Study of Depression and Anxiety (NESDA). MDD diagnoses were based on DSM-IV criteria and the Inventory of Depressive Symptomatology questionnaire measured endorsement of 30 symptoms. Serum protein levels were detected using a multi-analyte platform (171 analytes, immunoassay, Myriad RBM DiscoveryMAP 250+). Proteomic clusters were computed using weighted correlation network analysis (WGCNA). Six proteomic clusters were identified, of which one was nominally significantly associated with current MDD (p = 9.62E-03, Bonferroni adj. p = 0.057). This cluster contained 21 analytes and was enriched with pathways involved in inflammation and metabolism [including C-reactive protein (CRP), leptin and insulin]. At the individual symptom level, this proteomic cluster was associated with ten symptoms, among which were five atypical, energy-related symptoms. After correcting for several health and lifestyle covariates, hypersomnia, increased appetite, panic and weight gain remained significantly associated with the cluster. Our findings support the idea that alterations in a network of proteins involved in inflammatory and metabolic processes are present in MDD, but these alterations map predominantly to clinical symptoms reflecting an imbalance between energy intake and expenditure.

Highlights

Major depressive disorder (MDD) is a leading cause of disability worldwide, with the number of people affected estimated at 322 million (Institute for Health Metrics and Evaluation, 2018)
By applying weighted correlation network analysis (WGCNA) on proteomic assay data, we identified six clusters of correlated proteins, from which one cluster was nominally associated with current major depressive disorder (MDD)
This cluster contained 21 proteins - including leptin, insulin and C-reactive protein (CRP) - mainly involved in inflammation and metabolism

Summary

Introduction

Major depressive disorder (MDD) is a leading cause of disability worldwide, with the number of people affected estimated at 322 million (Institute for Health Metrics and Evaluation, 2018). The association with biological dysregulations may vary as a function of depression heterogeneity; patients with the same MDD diagnosis endorse very different symptom profiles that, in turn, may be differentially related to underlying biological dysregulations. Recent evidence suggests that the link with adverse inflammatory and metabolic dysregulations seems stronger in patients reporting atypical depressive symptoms characterized by altered energy homeostasis, such as hypersomnia, increased appetite, weight gain, energy loss and leaden paralysis (Glaus et al, 2013; Lamers et al, 2020, 2013; Milaneschi et al, 2016; van Reedt Dortland, Giltay, van Veen, van Pelt, et al, 2010; van Reedt Dortland, Giltay, van Veen, Zitman, et al, 2010). We studied clusters of proteins and examined their relation with MDD and individual depressive symptoms. Our findings support the idea that alterations in a network of proteins involved in inflammatory and metabolic processes are present in MDD, but these alterations map predominantly to clinical symptoms reflecting an imbalance between energy intake and expenditure

Methods

Results

Conclusion