Comprehensive Summary
This study by Tao et al. investigates how chronic stress influences depression severity through behavioral and molecular mechanisms, focusing on cortisol and inflammatory cytokines, particularly interleukin-17 (IL-17). Researchers used a translational approach combining a mouse model of chronic unpredictable stress (CUS) and a human clinical cohort. Mice were exposed to varying stress frequencies and assessed for depressive-like behaviors and serum markers, while plasma cortisol and cytokine levels were measured in 239 human participants across healthy, moderate, and severe depression groups. The study found that greater stress intensity in mice correlated with longer immobility times in behavioral tests and higher corticosterone and IL-17 levels, showing a dose-response relationship. In humans, plasma cortisol and IL-17 increased with depression severity and were significantly correlated with Hamilton Depression Rating Scale (HAMD-17) scores. A neural network model using these two markers accurately classified depression severity, highlighting their diagnostic potential. The discussion emphasizes that chronic stress intensity amplifies depression progression through dysregulation of the hypothalamic-pituitary-adrenal axis and immune activation, with cortisol and IL-17 acting as biological bridges between stress and mood disorders.
Outcomes and Implications
This research is important because it identifies cortisol and IL-17 as potential biomarkers for monitoring depression severity and stress-related disease progression. The findings offer a biological framework linking chronic stress to depressive pathology, which could advance precision psychiatry by integrating physiological measures into mental health assessment. Clinically, measuring plasma cortisol and IL-17 may help in early detection, stratification, and treatment response monitoring in depression. The study also introduces a machine-learning diagnostic model that, with further validation, could aid clinicians in objectively distinguishing depression stages. Although large-scale, multi-center studies are needed for implementation, the authors suggest that integrating such biomarkers into clinical practice could become feasible within the next few years as diagnostic assays and predictive models mature.