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

Sarah R Vreijling; Cherise R Chin Fatt; Leanne M Williams; Alan F Schatzberg; Tim Usherwood; Charles B Nemeroff; A John Rush; Rudolf Uher; Katherine J Aitchison; Ole Köhler-Forsberg; Marcella Rietschel; Madhukar H Trivedi; Manish K Jha; Brenda W J H Penninx; Aartjan T F Beekman; Rick Jansen; Femke Lamers

doi:10.1192/bjp.2023.148

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

Br J Psychiatry. 2024 Mar;224(3):89-97. doi: 10.1192/bjp.2023.148.

Authors

Sarah R Vreijling¹, Cherise R Chin Fatt², Leanne M Williams³, Alan F Schatzberg³, Tim Usherwood⁴, Charles B Nemeroff⁵, A John Rush⁶, Rudolf Uher⁷, Katherine J Aitchison⁸, Ole Köhler-Forsberg⁹, Marcella Rietschel¹⁰, Madhukar H Trivedi², Manish K Jha², Brenda W J H Penninx¹¹, Aartjan T F Beekman¹¹, Rick Jansen¹², Femke Lamers¹

Affiliations

¹ Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; and Mental Health Program, Amsterdam Public Health, Amsterdam, The Netherlands.
² Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, Texas, USA.
³ Department of Psychiatry and Behavioral Sciences, Stanford School of Medicine, Stanford University, Stanford, California, USA.
⁴ Department of General Practice, Westmead Clinical School, University of Sydney, Sydney, Australia; Westmead Applied Research Centre, Faculty of Medicine and Health, University of Sydney, Sydney, Australia; and George Institute for Global Health, Sydney, Australia.
⁵ Department of Psychiatry and Behavioral Sciences, Dell Medical School, University of Texas, Austin, Texas, USA.
⁶ Department of Psychiatry and Behavioral Health, Duke School of Medicine, Durham, North Carolina, USA; and Duke-National University of Singapore, Singapore, Singapore.
⁷ Department of Psychiatry, Dalhousie University, Halifax, Nova Scotia, Canada.
⁸ Departments of Psychiatry & Medical Genetics, College of Health Sciences, University of Alberta, Edmonton, Alberta, Canada; Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada; and Women and Children's Research Institute, University of Alberta, Edmonton, Alberta, Canada.
⁹ Psychosis Research Unit, Aarhus University Hospital Psychiatry, Aarhus, Denmark; and Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
¹⁰ Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Faculty of Medicine Mannheim, University of Heidelberg, Mannheim, Germany.
¹¹ Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; Mental Health Program, Amsterdam Public Health, Amsterdam, The Netherlands; and Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam Neuroscience, Amsterdam, The Netherlands.
¹² Department of Psychiatry, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands; and Mood, Anxiety, Psychosis, Sleep & Stress Program, Amsterdam Neuroscience, Amsterdam, The Netherlands.

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 outcome (change in depressive symptom severity) and logistic regressions on secondary outcomes (response and remission) were conducted for the individual trial data-sets and pooled using random-effects meta-analyses.

Results: Although AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, β_pooled = 0.06, P = 0.049, 95% CI 0.0001-0.12, I² = 3.61%); this was also found for an IMD index combining these features (n = 372, β_pooled = 0.12, s.e. = 0.12, P = 0.031, 95% CI 0.01-0.22, I²= 23.91%), with a higher - but still small - effect size compared with CRP. Confining analyses to selective serotonin reuptake inhibitor users indicated larger effects of CRP (β_pooled = 0.16) and the IMD index (β_pooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.

Conclusions: Depressive symptoms of people with more IMD features improved less when treated with antidepressants. However, clinical relevance is limited owing to small effect sizes in inconsistent associations. Whether these patients would benefit more from treatments targeting immunometabolic pathways remains to be investigated.

Keywords: Antidepressants; depressive disorders; inflammation; profiling; treatment.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Antidepressive Agents* / therapeutic use
Depression* / drug therapy
Humans
Selective Serotonin Reuptake Inhibitors / pharmacology
Selective Serotonin Reuptake Inhibitors / therapeutic use
Treatment Outcome

Substances

Antidepressive Agents
Selective Serotonin Reuptake Inhibitors

Abstract

Publication types

MeSH terms

Substances

Grants and funding