Features of immunometabolic depression as predictors of antidepressant treatment outcomes

• Verfasst von: Vreijling, Sarah R. [VerfasserIn]
• Verfasst von: Fatt, Cherise R. Chin [VerfasserIn]
• Verfasst von: Williams, Leanne M. [VerfasserIn]
• Verfasst von: Schatzberg, Alan F. [VerfasserIn]
• Verfasst von: Usherwood, Tim [VerfasserIn]
• Verfasst von: Nemeroff, Charles B. [VerfasserIn]
• Verfasst von: Rush, A. John [VerfasserIn]
• Verfasst von: Uher, Rudolf [VerfasserIn]
• Verfasst von: Aitchison, Katherine J. [VerfasserIn]
• Verfasst von: Köhler-Forsberg, Ole [VerfasserIn]
• Verfasst von: Rietschel, Marcella [VerfasserIn]
• Verfasst von: Trivedi, Madhukar H. [VerfasserIn]
• Verfasst von: Jha, Manish K. [VerfasserIn]
• Verfasst von: Penninx, Brenda W. J. H. [VerfasserIn]
• Verfasst von: Beekman, Aartjan T. F. [VerfasserIn]
• Verfasst von: Jansen, Rick [VerfasserIn]
• Verfasst von: Lamers, Femke [VerfasserIn]
• Titel: Features of immunometabolic depression as predictors of antidepressant treatment outcomes
• Titelzusatz: pooled analysis of four clinical trials
• Verf.angabe: 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 and Femke Lamers
• E-Jahr: 2024
• Jahr: March 2024
• Umfang: 9 S.
• Fussnoten: Online veröffentlicht: 22. Dezember 2023 ; Gesehen am 14.10.2024
• Titel Quelle: Enthalten in: The British journal of psychiatry
• Ort Quelle: Cambridge : Cambridge University Press, 1963
• Jahr Quelle: 2024
• Band/Heft Quelle: 224(2024), 3 vom: März, Seite 89-97
• ISSN Quelle: 1472-1465
• DOI: doi:10.1192/bjp.2023.148
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Antidepressants
• Sach-SW: depressive disorders
• Sach-SW: inflammation
• Sach-SW: profiling
• Sach-SW: treatment
• K10plus-PPN: 1905545169

Abstract

BackgroundProfiling 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.AimsTo test the hypothesis that baseline IMD features predict poorer treatment outcomes with antidepressants.MethodData 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.ResultsAlthough AES severity and BMI did not predict changes in depressive symptom severity, higher baseline CRP predicted smaller reductions in depressive symptoms (n = 376, βpooled = 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, βpooled = 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 (βpooled = 0.16) and the IMD index (βpooled = 0.20). Baseline IMD features, both separately and combined, did not predict response or remission.ConclusionsDepressive 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.