우울증 환자의 치료 반응성에 따른 염증성 사이토카인 차이: 예비 연구

Abstract

Objectives: Dysregulation of inflammatory cytokines has been suggested as a key component in the pathophysiology of major depressive disorder (MDD). However, studies exploring inflammatory biomarkers to predict treatment response have yielded inconsistent results. This study aimed to identify cytokines that differentiate between treatment-resistant depression (TRD), treatment-responsive depression (non-TRD), and healthy controls (HC).

Methods: Thirty participants (TRD=10, non-TRD=10, HC=10) were included. Plasma levels of 92 inflammation-related cytokines were analyzed using the OlinkⓇ Target 96 Inflammation panel. Depression severity was measured using the 17-item Hamilton Depression Rating Scale (HDRS-17). Group differences were tested by analysis of variance or non-parametric equivalents. Correlations between cytokines and HDRS scores were assessed by Pearson or Spearman coefficients.

Results: Of the 92 analyzed cytokines, 27 showed significant differences (p<0.05). Several cytokines, including tumor necrosis factor related apoptosis inducing ligand (TRAIL), oncostatin M (OSM), differed significantly in both patient groups compared to HC. Notably, adenosine deaminase (ADA) was the only marker that distinguished the TRD group from both the non-TRD (p=0.046) and HC groups (p=0.002). Tumor necrosis factor superfamily member 14 (TNFSF14) levels were positively associated with baseline HDRS scores (ρ=0.494, p=0.027), while extracellular newly inducible receptor for advanced glycation end-products binding protein (EN-RAGE) levels were positively correlated with the change in HDRS scores (r=0.484, p=0.031); however, these correlations were reduced to trend-level after Bonferroni correction.

Conclusions: Our findings suggest that distinct inflammatory profiles exist among different subtypes of depression, although these preliminary results from a small sample require validation in larger, longitudinal cohorts. Specifically, ADA may serve as a potential biomarker for identifying TRD. Peripheral inflammatory biomarkers may help predict treatment response and guide personalized treatment in MDD.