Inflammatory biomarkers as predictors for unlocking antidepressant efficacy: Assessing predictive value and risk...

Major depressive disorder (MDD) is characterized by significant heterogeneity in treatment response, with inflammation hypothesized to play a role in its pathophysiology. Peripheral inflammatory biomarkers, such as the neutrophil-to-lymphocyte ratio (NLR) and C-reactive protein (CRP), may predict antidepressant efficacy. This study investigated the association between baseline inflammatory biomarkers, their changes, and antidepressant treatment outcomes in patients with MDD.

A prospective longitudinal cohort study in Qatar recruited 123 MDD outpatients (aged 18-64). Baseline assessments included NLR, CRP, monocyte-to-lymphocyte ratio (MLR), and platelet-to-lymphocyte ratio (PLR). Depression severity was measured via the Zung Self-Rating Depression Scale (ZSRS) at baseline and 12 weeks post-treatment. Statistical analyses, including multiple regression and Random Forest machine learning models, identified predictors of antidepressant response.

Improvement in depressive symptoms was associated with female sex, higher mean corpuscular volume (MCV), lower absolute neutrophil count (ANC), and higher eosinophil counts. However, changes in NLR, MLR, PLR, and CRP did not predict treatment response. Folate levels and PLR were identified by the machine learning model as top predictors, suggesting potential utility as biomarkers for response classification. Our study identified predictors of improvement in suicidal ideation, including hematological markers (lower RBC, higher eosinophils, lower monocytes), younger age, female sex, medical comorbidities, and longer assessment intervals.

Baseline ANC and eosinophil count may help stratify MDD treatment outcomes, though post-treatment biomarker changes were not linked to symptom improvement. Our findings highlight suicidality as a distinct pathology within depression, necessitating tailored interventions. This study highlights the complexity of inflammation in depression and suicidality, emphasizing the need for advanced biomarkers utilization in precision medicine and personalized psychiatry treatment.