Biomarkers associated with treatment outcome in young people with depression: A systematic review

Studies in adults have identified potential biomarkers for therapeutic response in depression; however, less is known in the context of depression in young people. Understanding predictive biomarkers could improve treatment selection, considering the low treatment response rates in adolescents and young adults with depression. This study aimed to investigate the relationship between cortisol, immune, and neural markers and treatment outcomes of psychotherapy or antidepressant treatment in young people with depression. We searched OVID Medline, EMBASE, Global Health, and PsycINFO for peer-reviewed studies investigating cortisol, immune and neuroimaging markers in adolescents diagnosed with MDD, ages 10-24, undergoing psychotherapy or antidepressant treatment. Of 25 included studies, 15 assessed neural markers, 8 assessed immune markers, 1 assessed cortisol markers, and 1 assessed neural and cortisol markers. Elevated cortisol response to stress was associated with greater symptom improvement following psychotherapy or antidepressant treatment. Findings regarding the impact of SSRIs on Interleukin (IL)- 1 beta, IL-4, IL-6 and tumour necrosis factor-alpha (TNF-α) are inconsistent, suggesting a potential inflammatory subtype but also highlighting the need for further research. Increased activation and connectivity in brain regions involved in emotion regulation and decreased connectivity of the default mode network were associated with better treatment outcomes after psychotherapy and antidepressant treatment. Our results suggest that neural and immune markers might be involved in the treatment mechanisms through which depression improves. We identified several potential biomarkers associated with treatment outcomes; however, the heterogeneity among study designs and biomarker measurements was high. Future research should investigate biomarkers of differential treatment response, replicate existing studies and focus on predictive analyses of treatment outcomes.