A network analysis of depressive symptoms and metabolomics.

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 ye

SUBMITTER: Rydin AO 

PROVIDER: S-EPMC10719687 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature