Serum Cortisol and Interleukin-6 as Key Biomarkers for a Diagnostic Algorithm of Combat-Related PTSD.

BACKGROUND: Post-traumatic stress disorder (PTSD) is a severe psychiatric condition prevalent among combat veterans. Its diagnosis is challenging due to the heterogeneity of clinical presentations and the complex interplay of pathogenic factors. OBJECTIVE: This study aimed to develop and validate a diagnostic algorithm for combat-related PTSD by integrating clinical data with a panel of biological markers associated with blood-brain barrier disruption (anti-GFAP and anti-NSE antibodies), HPA axis dysfunction (cortisol), and neuroinflammation (IL-6, IL-8). METHODS: A total of 721 male participants were enrolled: 434 veterans with PTSD (F43.1), 147 combat veterans without PTSD, and 140 non-combat military controls. All participants underwent clinical and psychometric assessment (Likert scale, HADS). Serum levels of biomarkers were measured using ELISA. Statistical analysis included non-parametric tests, correlation analysis, and binary logistic regression with Wald's method to build a predictive model. RESULTS: The binary logistic regression model identified cortisol and IL-6 as the most significant predictors of PTSD. The final algorithm, based on a cortisol level below 199.8 nmol/L and an IL-6 level above 0.002438 pg/mL, correctly classified 78% of patients (AUC = 0.724, 95% CI [0.669, 0.779]). Furthermore, levels of IL-4, IL-8, and cortisol positively correlated with the severity of combat stress factors, independent of physical injuries. CONCLUSIONS: We developed a novel diagnostic algorithm for combat-related PTSD based on cortisol and IL-6 levels, demonstrating high accuracy. The correlation between neuroinflammatory markers and the severity of combat exposure suggests their role as primary indicators of stress response, highlighting their utility for early risk identification and targeted interventions.