Dataset Information

Dissection of depression heterogeneity using proteomic clusters.

ABSTRACT:

Background

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.

Methods

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).

Results

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.

Conclusions

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.

SUBMITTER: van Haeringen M

PROVIDER: S-EPMC10235664 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Publications

Dissection of depression heterogeneity using proteomic clusters.

van Haeringen Marije M Milaneschi Yuri Y Lamers Femke F Penninx Brenda W J H BWJH Jansen Rick R

Psychological medicine 20220118 7

<h4>Background</h4>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.<h4>Methods</h4>The sample consisted of 1621 subj ...[more]

PMID: 35039097

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Dataset Information

Dissection of depression heterogeneity using proteomic clusters.

Background

Methods

Results

Conclusions

Publications

Dissection of depression heterogeneity using proteomic clusters.

Similar Datasets

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