Features of immunometabolic depression as predictors of antidepressant treatment outcomes: pooled analysis of four clinical trials.

ABSTRACT:

Background

Profiling patients on a proposed 'immunometabolic depression' (IMD) dimension, described as a cluster of atypical depressive symptoms related to energy regulation and immunometabolic dysregulations, may optimise personalised treatment.

Aims

To test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.

Method

Data on 2551 individuals with depression across the iSPOT-D (n = 967), CO-MED (n = 665), GENDEP (n = 773) and EMBARC (n = 146) clinical trials were used. Predictors included baseline severity of atypical energy-related symptoms (AES), body mass index (BMI) and C-reactive protein levels (CRP, three trials only) separately and aggregated into an IMD index. Mixed models on the primary

SUBMITTER: Vreijling SR 

PROVIDER: S-EPMC10884825 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature