Major depressive disorder and serum inflammatory biomarkers as predictors of reward processing dysfunction in an American Indian sample.

Abstract

Introduction: American Indians (AI) experience chronic stressors which may be associated with disproportionate prevalence rates of major depressive disorder (MDD). Stress affects mental health through increased inflammatory processes and has been associated with increased risk of MDD and disruptions to reward processing. This study investigated the role of inflammation in reward processing disruptions among AI individuals with lifetime MDD; a population at heightened risk due to chronic stressors.

Method: Participants (n=73) completed a monetary incentive delay task during simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI). Blood samples were analyzed for pro-inflammatory (tumor necrosis factor [TNF], interleukin-6 [IL-6], C-reactive protein [CRP]) and anti-inflammatory (interleukin-10 [IL-10]) biomarkers. Depression severity was assessed using the Patient-Reported Outcomes Measurement Information System (PROMIS) depression scale. Covariates were included and assessed from self-report measures.

Results: Regression analyses revealed that elevated TNF concentrations and sex were associated with reduced activation across subregions of the basal ganglia during gain anticipation. Similarly, TNF and CRP concentrations, as well as medication, were associated with reduced activation within basal ganglia subregions across loss anticipation. IL-10, IL-6, and P300 showed limited predictive value for neural responses.

Conclusions: These findings suggest that inflammation may contribute to reward processing disruptions by impairing striatal function amongst a sample with lifetime MDD. The observed associations underscore the importance of inflammation's potential role and association in the pathophysiology of MDD, particularly in contexts of chronic stress. This study highlights the need for addressing the disproportionate mental health burden among AI communities through a biopsychosocial approach.