The combination of serum bdnf, cortisol and ifn-gamma can assist the diagnosis of major depressive disorder

Abstract

Purpose: Misdiagnosis and ineffective treatment are common in major depressive disorder (MDD) in current clinical practice, while the combination of various serum proteins may assist the correct diagnosis. The study aimed to explore whether the combination of serum inflammatory, stress, and neurotrophic factors could be helpful for the diagnosis of MDD and to investigate the predictors associated with early symptom improvements. Patients and Methods: Baseline serum levels of C-reactive protein (CRP), interleukin (IL)-6, IL-10, IL-1beta, tumor necrosis factor (TNF)-alpha, interferon (INF)-gamma, cortisol, and brain-derived neurotrophic factor (BDNF) were detected in 30 MDD patients and 30 age-and gender-matched healthy controls. 17-item Hamilton Depression Rating Scale (HAMD-17) and Hamilton Anxiety Rating Scale (HAMA) were applied to assess symptoms both at baseline and two weeks after antidepressant treatment. Stepwise multiple linear regression was employed to identify the early efficacy predictors, and a logistic regression model was built with the above serum proteins. The area under the receiver operating characteristic (AUC) curve was calculated to evaluate the model’s diagnostic power. Results: Multiple linear regression revealed that baseline scores of retardation (β = −0.432, P = 0.012) and psychological anxiety (β = −0.423, P = 0.014) factors were negatively associated with the reduction rate of HAMD-17. A simple and efficient diagnostic model using serum BDNF, cortisol, and IFN-gamma levels was established by the forward stepwise logistic regression, and the model achieved an AUC of 0.884, with 86.7% sensitivity and 83.3% specificity. Conclusion: The results showed that combining serum BDNF, cortisol and IFN-gamma could aid the diagnosis of MDD, while baseline retardation and psychological anxiety may predict the poor early symptom improvement.