A network analysis of depressive symptoms and metabolomics

Arja O Rydin*, Yuri Milaneschi, Rick Quax, Jie Li, Jos A Bosch, Robert A Schoevers, Erik J Giltay, Brenda W J H Penninx, Femke Lamers

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
20 Downloads (Pure)

Abstract

BACKGROUND: Depression is associated with metabolic alterations including lipid dysregulation, whereby associations may vary across individual symptoms. Evaluating these associations using a network perspective yields a more complete insight than single outcome-single predictor models.

METHODS: We used data from the Netherlands Study of Depression and Anxiety ( N = 2498) and leveraged networks capturing associations between 30 depressive symptoms (Inventory of Depressive Symptomatology) and 46 metabolites. Analyses involved 4 steps: creating a network with Mixed Graphical Models; calculating centrality measures; bootstrapping for stability testing; validating central, stable associations by extra covariate-adjustment; and validation using another data wave collected 6 years later.

RESULTS: The network yielded 28 symptom-metabolite associations. There were 15 highly-central variables (8 symptoms, 7 metabolites), and 3 stable links involving the symptoms Low energy (fatigue), and Hypersomnia. Specifically, fatigue showed consistent associations with higher mean diameter for VLDL particles and lower estimated degree of (fatty acid) unsaturation. These remained present after adjustment for lifestyle and health-related factors and using another data wave.

CONCLUSIONS: The somatic symptoms Fatigue and Hypersomnia and cholesterol and fatty acid measures showed central, stable, and consistent relationships in our network. The present analyses showed how metabolic alterations are more consistently linked to specific symptom profiles.

Original languageEnglish
Pages (from-to)7385–7394
Number of pages10
JournalPsychological Medicine
Volume53
Issue number15
Early online date24-Apr-2023
DOIs
Publication statusPublished - Nov-2023

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